Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLM...Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.展开更多
This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to use...This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.展开更多
Fundamental physics often confronts complex symbolic problems with few guiding exemplars or established principles.While artificial intelligence(AI)offers promise,its typical need for vast datasets to learn from hinde...Fundamental physics often confronts complex symbolic problems with few guiding exemplars or established principles.While artificial intelligence(AI)offers promise,its typical need for vast datasets to learn from hinders its use in these information-scarce frontiers.We introduce learning at criticality(LaC),a reinforcement learning scheme that tunes large language models(LLMs)to a sharp learning transition,addressing this information scarcity.At this transition,LLMs achieve peak generalization from minimal data,exemplified by 7-digit base-7 addition-a test of nontrivial arithmetic reasoning.To elucidate this peak,we analyze a minimal concept-network model designed to capture the essence of how LLMs might link tokens.Trained on a single exemplar,this model also undergoes a sharp learning transition.This transition exhibits hallmarks of a second-order phase transition,notably power-law distributed solution path lengths.At this critical point,the system maximizes a“critical thinking pattern”crucial for generalization,enabled by the underlying scale-free exploration.This suggests LLMs reach peak performance by operating at criticality,where such explorative dynamics enable the extraction of underlying operational rules.We demonstrate LaC in quantum field theory:an 8B-parameter LLM,tuned to its critical point by LaC using a few exemplars of symbolic Matsubara sums,solves unseen,higher-order problems,significantly outperforming far larger models.LaC thus leverages critical phenomena,a physical principle,to empower AI for complex,data-sparse challenges in fundamental physics.展开更多
BACKGROUND Gastrointestinal diseases have complex etiologies and clinical presentations.An accurate diagnosis requires physicians to integrate diverse information,including medical history,laboratory test results,and ...BACKGROUND Gastrointestinal diseases have complex etiologies and clinical presentations.An accurate diagnosis requires physicians to integrate diverse information,including medical history,laboratory test results,and imaging findings.Existing artificial intelligence-assisted diagnostic tools are limited to single-modality information,resulting in recommendations that are often incomplete and may be associated with clinical or legal risks.AIM To develop and evaluate a collaborative multimodal large language model(LLM)framework for clinical decision-making in digestive diseases.METHODS In this observational study,DeepGut,a multimodal LLM collaborative diagnostic framework,was developed to integrate four distinct large models into a four-tiered structure.The framework sequentially accomplishes multimodal infor-mation extraction,logical“chain”construction,diagnostic and treatment suggestion generation,and risk analysis.The model was evaluated using objective metrics,which assess the reliability and comprehensiveness of model-generated results,and subjective expert opinions,which examine the effectiveness of the framework in assisting physicians.RESULTS The diagnostic and treatment recommendations generated by the DeepGut framework achieved exceptional performance,with a diagnostic accuracy of 97.8%,diagnostic completeness of 93.9%,treatment plan accuracy of 95.2%,and treatment plan completeness of 98.0%,significantly surpassing the capabilities of single-modal LLM-based diagnostic tools.Experts evaluating the framework commended the completeness,relevance,and logical coherence of its outputs.However,the collaborative multimodal LLM approach resulted in increased input and output token counts,leading to higher computational costs and extended diagnostic times.CONCLUSION The framework achieves successful integration of multimodal diagnostic data,demonstrating enhanced performance enabled by multimodal LLM collaboration,which opens new horizons for the clinical application of artificial intelligence-assisted technology.展开更多
Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and langua...Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.展开更多
The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation capabilities.De...The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation capabilities.Despite their transformative impact in fields such as machine translation and intelligent dialogue systems,LLMs face significant challenges.These challenges include safety,security,and privacy concerns that undermine their trustworthiness and effectiveness,such as hallucinations,backdoor attacks,and privacy leakage.Previous works often conflated safety issues with security concerns.In contrast,our study provides clearer and more reasonable definitions for safety,security,and privacy within the context of LLMs.Building on these definitions,we provide a comprehensive overview of the vulnerabilities and defense mechanisms related to safety,security,and privacy in LLMs.Additionally,we explore the unique research challenges posed by LLMs and suggest potential avenues for future research,aiming to enhance the robustness and reliability of LLMs in the face of emerging threats.展开更多
Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, ...Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.展开更多
ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential sec...ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future directions.Through this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.展开更多
In recent years,Volunteered Geographic Information(VGI)has emerged as a crucial source of mapping data,contributed by users through crowdsourcing platforms such as OpenStreetMap.This paper presents a novel approach th...In recent years,Volunteered Geographic Information(VGI)has emerged as a crucial source of mapping data,contributed by users through crowdsourcing platforms such as OpenStreetMap.This paper presents a novel approach that Integrates Large Language Models(LLMs)into a fully automated mapping workflow,utilizing VGI data.The process leverages Prompt Engineering,which involves designing and optimizing input instructions to ensure the LLM produces desired mapping outputs.By constructing precise and detailed prompts,LLM agents are able to accurately interpret mapping requirements,and autonomously extract,analyze,and process VGI geospatial data.They dynamically interact with mapping tools to automate the entire mapping process—from data acquisition to map generation.This approach significantly streamlines the creation of high-quality mapping outputs,reducing the time and resources typically required for such tasks.Moreover,the system lowers the barrier for non-expert users,enabling them to generate accurate maps without extensive technical expertise.Through various case studies,we demonstrate the LLM application across different mapping scenarios,highlighting its potential to enhance the efficiency,accuracy,and accessibility of map production.The results suggest that LLM-powered mapping systems can not only optimize VGI data processing but also expand the usability of ubiquitous mapping across diverse fields,including urban planning and infrastructure development.展开更多
Purpose:Evaluating the quality of academic journal articles is a time consuming but critical task for national research evaluation exercises,appointments and promotion.It is therefore important to investigate whether ...Purpose:Evaluating the quality of academic journal articles is a time consuming but critical task for national research evaluation exercises,appointments and promotion.It is therefore important to investigate whether Large Language Models(LLMs)can play a role in this process.Design/methodology/approach:This article assesses which ChatGPT inputs(full text without tables,figures,and references;title and abstract;title only)produce better quality score estimates,and the extent to which scores are affected by ChatGPT models and system prompts.Findings:The optimal input is the article title and abstract,with average ChatGPT scores based on these(30 iterations on a dataset of 51 papers)correlating at 0.67 with human scores,the highest ever reported.ChatGPT 4o is slightly better than 3.5-turbo(0.66),and 4o-mini(0.66).Research limitations:The data is a convenience sample of the work of a single author,it only includes one field,and the scores are self-evaluations.Practical implications:The results suggest that article full texts might confuse LLM research quality evaluations,even though complex system instructions for the task are more effective than simple ones.Thus,whilst abstracts contain insufficient information for a thorough assessment of rigour,they may contain strong pointers about originality and significance.Finally,linear regression can be used to convert the model scores into the human scale scores,which is 31%more accurate than guessing.Originality/value:This is the first systematic comparison of the impact of different prompts,parameters and inputs for ChatGPT research quality evaluations.展开更多
BACKGROUND Inflammatory bowel disease(IBD)is a global health burden that affects millions of individuals worldwide,necessitating extensive patient education.Large language models(LLMs)hold promise for addressing patie...BACKGROUND Inflammatory bowel disease(IBD)is a global health burden that affects millions of individuals worldwide,necessitating extensive patient education.Large language models(LLMs)hold promise for addressing patient information needs.However,LLM use to deliver accurate and comprehensible IBD-related medical information has yet to be thoroughly investigated.AIM To assess the utility of three LLMs(ChatGPT-4.0,Claude-3-Opus,and Gemini-1.5-Pro)as a reference point for patients with IBD.METHODS In this comparative study,two gastroenterology experts generated 15 IBD-related questions that reflected common patient concerns.These questions were used to evaluate the performance of the three LLMs.The answers provided by each model were independently assessed by three IBD-related medical experts using a Likert scale focusing on accuracy,comprehensibility,and correlation.Simultaneously,three patients were invited to evaluate the comprehensibility of their answers.Finally,a readability assessment was performed.RESULTS Overall,each of the LLMs achieved satisfactory levels of accuracy,comprehensibility,and completeness when answering IBD-related questions,although their performance varies.All of the investigated models demonstrated strengths in providing basic disease information such as IBD definition as well as its common symptoms and diagnostic methods.Nevertheless,when dealing with more complex medical advice,such as medication side effects,dietary adjustments,and complication risks,the quality of answers was inconsistent between the LLMs.Notably,Claude-3-Opus generated answers with better readability than the other two models.CONCLUSION LLMs have the potential as educational tools for patients with IBD;however,there are discrepancies between the models.Further optimization and the development of specialized models are necessary to ensure the accuracy and safety of the information provided.展开更多
AIM:To assess the possibility of using different large language models(LLMs)in ocular surface diseases by selecting five different LLMS to test their accuracy in answering specialized questions related to ocular surfa...AIM:To assess the possibility of using different large language models(LLMs)in ocular surface diseases by selecting five different LLMS to test their accuracy in answering specialized questions related to ocular surface diseases:ChatGPT-4,ChatGPT-3.5,Claude 2,PaLM2,and SenseNova.METHODS:A group of experienced ophthalmology professors were asked to develop a 100-question singlechoice question on ocular surface diseases designed to assess the performance of LLMs and human participants in answering ophthalmology specialty exam questions.The exam includes questions on the following topics:keratitis disease(20 questions),keratoconus,keratomalaciac,corneal dystrophy,corneal degeneration,erosive corneal ulcers,and corneal lesions associated with systemic diseases(20 questions),conjunctivitis disease(20 questions),trachoma,pterygoid and conjunctival tumor diseases(20 questions),and dry eye disease(20 questions).Then the total score of each LLMs and compared their mean score,mean correlation,variance,and confidence were calculated.RESULTS:GPT-4 exhibited the highest performance in terms of LLMs.Comparing the average scores of the LLMs group with the four human groups,chief physician,attending physician,regular trainee,and graduate student,it was found that except for ChatGPT-4,the total score of the rest of the LLMs is lower than that of the graduate student group,which had the lowest score in the human group.Both ChatGPT-4 and PaLM2 were more likely to give exact and correct answers,giving very little chance of an incorrect answer.ChatGPT-4 showed higher credibility when answering questions,with a success rate of 59%,but gave the wrong answer to the question 28% of the time.CONCLUSION:GPT-4 model exhibits excellent performance in both answer relevance and confidence.PaLM2 shows a positive correlation(up to 0.8)in terms of answer accuracy during the exam.In terms of answer confidence,PaLM2 is second only to GPT4 and surpasses Claude 2,SenseNova,and GPT-3.5.Despite the fact that ocular surface disease is a highly specialized discipline,GPT-4 still exhibits superior performance,suggesting that its potential and ability to be applied in this field is enormous,perhaps with the potential to be a valuable resource for medical students and clinicians in the future.展开更多
The rapid advancement of Artificial Intelligence(AI)and Large Language Models(LLMs)has led to their increasing integration into various domains,from text generation and translation to question-answering.However,a crit...The rapid advancement of Artificial Intelligence(AI)and Large Language Models(LLMs)has led to their increasing integration into various domains,from text generation and translation to question-answering.However,a critical question remains:do these sophisticated models,much like humans,exhibit susceptibility to cognitive biases?Understanding the presence and nature of such biases in AI is paramount for assessing their reliability,enhancing their performance,and predicting their societal impact.This research specifically investigates the susceptibility of Google’s Gemini 1.5 Pro and DeepSeek,two prominent LLMs,to framing effects and confirmation bias.The study meticulously designed a series of experimental trials,systematically manipulating information proportions and presentation orders to evaluate these biases.In the framing effect experiment,a genetic testing decision-making scenario was constructed.The proportion of positive and negative information(e.g.,20%,50%,or 80%positive)and their presentation order were varied.The models’inclination towards undergoing genetic testing was recorded.For the confirmation bias experiment,two reports-one positive and one negative-about“RoboTaxi”autonomous vehicles were provided.The proportion of erroneous information within these reports(10%,30%,and 50%)and their presentation order were systematically altered,and the models’support for each report was assessed.The findings demonstrate that both Gemini 1.5 Pro and DeepSeek are susceptible to framing effects.In the genetic testing scenario,their decision-making was primarily influenced by the proportion of positive and negative information presented.When the proportion of positive information was higher,both models showed a greater inclination to recommend or proceed with genetic testing.Conversely,a higher proportion of negative information led to greater caution or a tendency not to recommend the testing.Importantly,the order in which this information was presented did not significantly influence their decisions in the framing effect scenarios.Regarding confirmation bias,the two models exhibited distinct behaviors.Gemini 1.5 Pro did not show an overall preference for either positive or negative reports.However,its judgments were significantly influenced by the order of information presentation,demonstrating a“recency effect,”meaning it tended to support the report presented later.The proportion of erroneous information within the reports had no significant impact on Gemini 1.5 Pro’s decisions.In contrast,DeepSeek exhibited an overall confirmation bias,showing a clear preference for positive reports.Similar to Gemini 1.5 Pro,DeepSeek’s decisions were also significantly affected by the order of information presentation,while the proportion of misinformation had no significant effect.These results reveal human-like cognitive vulnerabilities in advanced LLMs,highlighting critical challenges to their reliability and objectivity in decision-making processes.Gemini 1.5 Pro’s sensitivity to presentation order and DeepSeek’s general preference for positive information,coupled with its sensitivity to order,underscore the need for careful evaluation of potential cognitive biases during the development and application of AI.The study suggests that effective measures are necessary to mitigate these biases and prevent potential negative societal impacts.Future research should include a broader range of models for comparative analysis and explore more complex interactive scenarios to further understand and address these phenomena.The findings contribute significantly to understanding the limitations and capabilities of current AI systems,guiding their responsible development,and anticipating their potential societal implications.展开更多
Smart contracts on the Ethereum blockchain continue to revolutionize decentralized applications (dApps) by allowing for self-executing agreements. However, bad actors have continuously found ways to exploit smart cont...Smart contracts on the Ethereum blockchain continue to revolutionize decentralized applications (dApps) by allowing for self-executing agreements. However, bad actors have continuously found ways to exploit smart contracts for personal financial gain, which undermines the integrity of the Ethereum blockchain. This paper proposes a computer program called SADA (Static and Dynamic Analyzer), a novel approach to smart contract vulnerability detection using multiple Large Language Model (LLM) agents to analyze and flag suspicious Solidity code for Ethereum smart contracts. SADA not only improves upon existing vulnerability detection methods but also paves the way for more secure smart contract development practices in the rapidly evolving blockchain ecosystem.展开更多
AIM:To investigate the capabilities of large language models(LLM)for providing information and diagnoses in the field of neuro-ophthalmology by comparing the performances of ChatGPT-3.5 and-4.0,Bard,and Bing.METHODS:E...AIM:To investigate the capabilities of large language models(LLM)for providing information and diagnoses in the field of neuro-ophthalmology by comparing the performances of ChatGPT-3.5 and-4.0,Bard,and Bing.METHODS:Each chatbot was evaluated for four criteria,namely diagnostic success rate for the described case,answer quality,response speed,and critical keywords for diagnosis.The selected topics included optic neuritis,nonarteritic anterior ischemic optic neuropathy,and Leber hereditary optic neuropathy.RESULTS:In terms of diagnostic success rate for the described cases,Bard was unable to provide a diagnosis.The success rates for the described cases increased in the order of Bing,ChatGPT-3.5,and ChatGPT-4.0.Further,ChatGPT-4.0 and-3.5 provided the most satisfactory answer quality for judgment by neuro-ophthalmologists,with their sets of answers resembling the sample set most.Bard was only able to provide ten differential diagnoses in three trials.Bing scored the lowest for the satisfactory standard.A Mann-Whitney test indicated that Bard was significantly faster than ChatGPT-4.0(Z=-3.576,P=0.000),ChatGPT-3.5(Z=-3.576,P=0.000)and Bing(Z=-2.517,P=0.011).ChatGPT-3.5 and-4.0 far exceeded the other two interfaces at providing diagnoses and were thus used to find the critical keywords for diagnosis.CONCLUSION:ChatGPT-3.5 and-4.0 are better than Bard and Bing in terms of answer success rate,answer quality,and critical keywords for diagnosis in ophthalmology.This study has broad implications for the field of ophthalmology,providing further evidence that artificial intelligence LLM can aid clinical decision-making through free-text explanations.展开更多
Dear Editor,This letter deals with automatically constructing an OPC UA information model(IM)aimed at enhancing data interoperability among heterogeneous system components within manufacturing automation systems.Empow...Dear Editor,This letter deals with automatically constructing an OPC UA information model(IM)aimed at enhancing data interoperability among heterogeneous system components within manufacturing automation systems.Empowered by the large language model(LLM),we propose a novel multi-agent collaborative framework to streamline the end-to-end OPC UA IM modeling process.Each agent is equipped with meticulously engineered prompt templates,augmenting their capacity to execute specific tasks.We conduct modeling experiments using real textual data to demonstrate the effectiveness of the proposed method,improving modeling efficiency and reducing the labor workload.展开更多
As large language models increasingly permeate medical workflows,a recent study evaluating ChatGPT 4.0’s performance in addressing patient queries about endoscopic submucosal dissection and endoscopic mucosal resecti...As large language models increasingly permeate medical workflows,a recent study evaluating ChatGPT 4.0’s performance in addressing patient queries about endoscopic submucosal dissection and endoscopic mucosal resection offers critical insights into three domains:Performance parity,cost democratization,and clinical readiness.The findings highlight ChatGPT’s high accuracy,completeness,and comprehensibility,suggesting potential cost efficiency in patient education.Yet,cost-effectiveness alone does not ensure clinical utility.Notably,the study relied exclusively on text-based prompts,omitting multimodal data such as photographs or endoscopic scans.This is a significant limitation in a visually driven field like endoscopy,where large language model performance may drop precipitously without image context.Without multimodal integration,artificial intelligence tools risk failing to capture key diagnostic signals,underscoring the need for cautious adoption and robust validation in clinical practice.展开更多
Preserving formal style in neural machine translation (NMT) is essential, yet often overlooked as an optimization objective of the training processes. This oversight can lead to translations that, though accurate, lac...Preserving formal style in neural machine translation (NMT) is essential, yet often overlooked as an optimization objective of the training processes. This oversight can lead to translations that, though accurate, lack formality. In this paper, we propose how to improve NMT formality with large language models (LLMs), which combines the style transfer and evaluation capabilities of an LLM and the high-quality translation generation ability of NMT models to improve NMT formality. The proposed method (namely INMTF) encompasses two approaches. The first involves a revision approach using an LLM to revise the NMT-generated translation, ensuring a formal translation style. The second approach employs an LLM as a reward model for scoring translation formality, and then uses reinforcement learning algorithms to fine-tune the NMT model to maximize the reward score, thereby enhancing the formality of the generated translations. Considering the substantial parameter size of LLMs, we also explore methods to reduce the computational cost of INMTF. Experimental results demonstrate that INMTF significantly outperforms baselines in terms of translation formality and translation quality, with an improvement of +9.19 style accuracy points in the German-to-English task and +2.16 COMET score in the Russian-to-English task. Furthermore, our work demonstrates the potential of integrating LLMs within NMT frameworks to bridge the gap between NMT outputs and the formality required in various real-world translation scenarios.展开更多
Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches ofte...Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches often rely on limited data sources and simplistic hypotheses,which restrict their ability to capture the multi-faceted nature of biological systems.This study introduces adaptive multi-view learning(AMVL),a novel methodology that integrates chemical-induced transcriptional profiles(CTPs),knowledge graph(KG)embeddings,and large language model(LLM)representations,to enhance drug repurposing predictions.AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning(MVL),matrix factorization,and ensemble optimization techniques to integrate heterogeneous multi-source data.Comprehensive evaluations on benchmark datasets(Fdata-set,Cdataset,and Ydataset)and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art(SOTA)methods,achieving superior accuracy in predicting drug-disease associations across multiple metrics.Literature-based validation further confirmed the model's predictive capabilities,with seven out of the top ten predictions corroborated by post-2011 evidence.To promote transparency and reproducibility,all data and codes used in this study were open-sourced,providing resources for pro-cessing CTPs,KG,and LLM-based similarity calculations,along with the complete AMVL algorithm and benchmarking procedures.By unifying diverse data modalities,AMVL offers a robust and scalable so-lution for accelerating drug discovery,fostering advancements in translational medicine and integrating multi-omics data.We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.展开更多
As large language models(LLMs)continue to demonstrate their potential in handling complex tasks,their value in knowledge-intensive industrial scenarios is becoming increasingly evident.Fault diagnosis,a critical domai...As large language models(LLMs)continue to demonstrate their potential in handling complex tasks,their value in knowledge-intensive industrial scenarios is becoming increasingly evident.Fault diagnosis,a critical domain in the industrial sector,has long faced the dual challenges of managing vast amounts of experiential knowledge and improving human-machine collaboration efficiency.Traditional fault diagnosis systems,which are primarily based on expert systems,suffer from three major limitations:(1)ineffective organization of fault diagnosis knowledge,(2)lack of adaptability between static knowledge frameworks and dynamic engineering environments,and(3)difficulties in integrating expert knowledge with real-time data streams.These systemic shortcomings restrict the ability of conventional approaches to handle uncertainty.In this study,we proposed an intelligent computer numerical control(CNC)fault diagnosis system,integrating LLMs with knowledge graph(KG).First,we constructed a comprehensive KG that consolidated multi-source data for structured representation.Second,we designed a retrievalaugmented generation(RAG)framework leveraging the KG to support multi-turn interactive fault diagnosis while incorporating real-time engineering data into the decision-making process.Finally,we introduced a learning mechanism to facilitate dynamic knowledge updates.The experimental results demonstrated that our system significantly improved fault diagnosis accuracy,outperforming engineers with two years of professional experience on our constructed benchmark datasets.By integrating LLMs and KG,our framework surpassed the limitations of traditional expert systems rooted in symbolic reasoning,offering a novel approach to addressing the cognitive paradox of unstructured knowledge modeling and dynamic environment adaptation in industrial settings.展开更多
文摘Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.
基金funded by the Office of the Vice-President for Research and Development of Cebu Technological University.
文摘This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.
基金supported by the National Key Research and Development Program of China(Grant No.2024YFA1408604 for K.C.and X.C.)the National Natural Science Foundation of China(Grant Nos.12047503,12447103 for K.C.and X.C.,12325501 for P.Z.,and 12275263 for Y.D.and S.H.)+1 种基金the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0301900 for Y.D.and S.H.)the Natural Science Foundation of Fujian Province of China(Grant No.2023J02032 for Y.D.and S.H.)。
文摘Fundamental physics often confronts complex symbolic problems with few guiding exemplars or established principles.While artificial intelligence(AI)offers promise,its typical need for vast datasets to learn from hinders its use in these information-scarce frontiers.We introduce learning at criticality(LaC),a reinforcement learning scheme that tunes large language models(LLMs)to a sharp learning transition,addressing this information scarcity.At this transition,LLMs achieve peak generalization from minimal data,exemplified by 7-digit base-7 addition-a test of nontrivial arithmetic reasoning.To elucidate this peak,we analyze a minimal concept-network model designed to capture the essence of how LLMs might link tokens.Trained on a single exemplar,this model also undergoes a sharp learning transition.This transition exhibits hallmarks of a second-order phase transition,notably power-law distributed solution path lengths.At this critical point,the system maximizes a“critical thinking pattern”crucial for generalization,enabled by the underlying scale-free exploration.This suggests LLMs reach peak performance by operating at criticality,where such explorative dynamics enable the extraction of underlying operational rules.We demonstrate LaC in quantum field theory:an 8B-parameter LLM,tuned to its critical point by LaC using a few exemplars of symbolic Matsubara sums,solves unseen,higher-order problems,significantly outperforming far larger models.LaC thus leverages critical phenomena,a physical principle,to empower AI for complex,data-sparse challenges in fundamental physics.
基金Supported by China Health Promotion Foundation Young Doctors’Research Foundation for Inflammatory Bowel DiseaseTaishan Scholars Program of Shandong Province,China,NO.tsqn202306343National Natural Science Foundation of China,No.82270580,No.82070552,No.82270578,and No.82300599.
文摘BACKGROUND Gastrointestinal diseases have complex etiologies and clinical presentations.An accurate diagnosis requires physicians to integrate diverse information,including medical history,laboratory test results,and imaging findings.Existing artificial intelligence-assisted diagnostic tools are limited to single-modality information,resulting in recommendations that are often incomplete and may be associated with clinical or legal risks.AIM To develop and evaluate a collaborative multimodal large language model(LLM)framework for clinical decision-making in digestive diseases.METHODS In this observational study,DeepGut,a multimodal LLM collaborative diagnostic framework,was developed to integrate four distinct large models into a four-tiered structure.The framework sequentially accomplishes multimodal infor-mation extraction,logical“chain”construction,diagnostic and treatment suggestion generation,and risk analysis.The model was evaluated using objective metrics,which assess the reliability and comprehensiveness of model-generated results,and subjective expert opinions,which examine the effectiveness of the framework in assisting physicians.RESULTS The diagnostic and treatment recommendations generated by the DeepGut framework achieved exceptional performance,with a diagnostic accuracy of 97.8%,diagnostic completeness of 93.9%,treatment plan accuracy of 95.2%,and treatment plan completeness of 98.0%,significantly surpassing the capabilities of single-modal LLM-based diagnostic tools.Experts evaluating the framework commended the completeness,relevance,and logical coherence of its outputs.However,the collaborative multimodal LLM approach resulted in increased input and output token counts,leading to higher computational costs and extended diagnostic times.CONCLUSION The framework achieves successful integration of multimodal diagnostic data,demonstrating enhanced performance enabled by multimodal LLM collaboration,which opens new horizons for the clinical application of artificial intelligence-assisted technology.
基金supported by National Natural Science Foundation of China(62376219 and 62006194)Foundational Research Project in Specialized Discipline(Grant No.G2024WD0146)Faculty Construction Project(Grant No.24GH0201148).
文摘Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.
基金supported by the National Key R&D Program of China under Grant No.2022YFB3103500the National Natural Science Foundation of China under Grants No.62402087 and No.62020106013+3 种基金the Sichuan Science and Technology Program under Grant No.2023ZYD0142the Chengdu Science and Technology Program under Grant No.2023-XT00-00002-GXthe Fundamental Research Funds for Chinese Central Universities under Grants No.ZYGX2020ZB027 and No.Y030232063003002the Postdoctoral Innovation Talents Support Program under Grant No.BX20230060.
文摘The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation capabilities.Despite their transformative impact in fields such as machine translation and intelligent dialogue systems,LLMs face significant challenges.These challenges include safety,security,and privacy concerns that undermine their trustworthiness and effectiveness,such as hallucinations,backdoor attacks,and privacy leakage.Previous works often conflated safety issues with security concerns.In contrast,our study provides clearer and more reasonable definitions for safety,security,and privacy within the context of LLMs.Building on these definitions,we provide a comprehensive overview of the vulnerabilities and defense mechanisms related to safety,security,and privacy in LLMs.Additionally,we explore the unique research challenges posed by LLMs and suggest potential avenues for future research,aiming to enhance the robustness and reliability of LLMs in the face of emerging threats.
文摘Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.
文摘ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future directions.Through this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.
基金National Natural Science Foundation of china(No.42371446)Natural Science Foundatiorof Hubei Province(No.2024AFD412)Fundamental Research Funds for National Universities,China University of Geosciences(Wuhan)(No.2024XLA17).
文摘In recent years,Volunteered Geographic Information(VGI)has emerged as a crucial source of mapping data,contributed by users through crowdsourcing platforms such as OpenStreetMap.This paper presents a novel approach that Integrates Large Language Models(LLMs)into a fully automated mapping workflow,utilizing VGI data.The process leverages Prompt Engineering,which involves designing and optimizing input instructions to ensure the LLM produces desired mapping outputs.By constructing precise and detailed prompts,LLM agents are able to accurately interpret mapping requirements,and autonomously extract,analyze,and process VGI geospatial data.They dynamically interact with mapping tools to automate the entire mapping process—from data acquisition to map generation.This approach significantly streamlines the creation of high-quality mapping outputs,reducing the time and resources typically required for such tasks.Moreover,the system lowers the barrier for non-expert users,enabling them to generate accurate maps without extensive technical expertise.Through various case studies,we demonstrate the LLM application across different mapping scenarios,highlighting its potential to enhance the efficiency,accuracy,and accessibility of map production.The results suggest that LLM-powered mapping systems can not only optimize VGI data processing but also expand the usability of ubiquitous mapping across diverse fields,including urban planning and infrastructure development.
文摘Purpose:Evaluating the quality of academic journal articles is a time consuming but critical task for national research evaluation exercises,appointments and promotion.It is therefore important to investigate whether Large Language Models(LLMs)can play a role in this process.Design/methodology/approach:This article assesses which ChatGPT inputs(full text without tables,figures,and references;title and abstract;title only)produce better quality score estimates,and the extent to which scores are affected by ChatGPT models and system prompts.Findings:The optimal input is the article title and abstract,with average ChatGPT scores based on these(30 iterations on a dataset of 51 papers)correlating at 0.67 with human scores,the highest ever reported.ChatGPT 4o is slightly better than 3.5-turbo(0.66),and 4o-mini(0.66).Research limitations:The data is a convenience sample of the work of a single author,it only includes one field,and the scores are self-evaluations.Practical implications:The results suggest that article full texts might confuse LLM research quality evaluations,even though complex system instructions for the task are more effective than simple ones.Thus,whilst abstracts contain insufficient information for a thorough assessment of rigour,they may contain strong pointers about originality and significance.Finally,linear regression can be used to convert the model scores into the human scale scores,which is 31%more accurate than guessing.Originality/value:This is the first systematic comparison of the impact of different prompts,parameters and inputs for ChatGPT research quality evaluations.
基金Supported by the China Health Promotion Foundation Young Doctors'Research Foundation for Inflammatory Bowel Disease,the Taishan Scholars Program of Shandong Province,China,No.tsqn202306343National Natural Science Foundation of China,No.82270578.
文摘BACKGROUND Inflammatory bowel disease(IBD)is a global health burden that affects millions of individuals worldwide,necessitating extensive patient education.Large language models(LLMs)hold promise for addressing patient information needs.However,LLM use to deliver accurate and comprehensible IBD-related medical information has yet to be thoroughly investigated.AIM To assess the utility of three LLMs(ChatGPT-4.0,Claude-3-Opus,and Gemini-1.5-Pro)as a reference point for patients with IBD.METHODS In this comparative study,two gastroenterology experts generated 15 IBD-related questions that reflected common patient concerns.These questions were used to evaluate the performance of the three LLMs.The answers provided by each model were independently assessed by three IBD-related medical experts using a Likert scale focusing on accuracy,comprehensibility,and correlation.Simultaneously,three patients were invited to evaluate the comprehensibility of their answers.Finally,a readability assessment was performed.RESULTS Overall,each of the LLMs achieved satisfactory levels of accuracy,comprehensibility,and completeness when answering IBD-related questions,although their performance varies.All of the investigated models demonstrated strengths in providing basic disease information such as IBD definition as well as its common symptoms and diagnostic methods.Nevertheless,when dealing with more complex medical advice,such as medication side effects,dietary adjustments,and complication risks,the quality of answers was inconsistent between the LLMs.Notably,Claude-3-Opus generated answers with better readability than the other two models.CONCLUSION LLMs have the potential as educational tools for patients with IBD;however,there are discrepancies between the models.Further optimization and the development of specialized models are necessary to ensure the accuracy and safety of the information provided.
基金Supported by National Natural Science Foundation of China(No.82160195,No.82460203)Degree and Postgraduate Education Teaching Reform Project of Jiangxi Province(No.JXYJG-2020-026).
文摘AIM:To assess the possibility of using different large language models(LLMs)in ocular surface diseases by selecting five different LLMS to test their accuracy in answering specialized questions related to ocular surface diseases:ChatGPT-4,ChatGPT-3.5,Claude 2,PaLM2,and SenseNova.METHODS:A group of experienced ophthalmology professors were asked to develop a 100-question singlechoice question on ocular surface diseases designed to assess the performance of LLMs and human participants in answering ophthalmology specialty exam questions.The exam includes questions on the following topics:keratitis disease(20 questions),keratoconus,keratomalaciac,corneal dystrophy,corneal degeneration,erosive corneal ulcers,and corneal lesions associated with systemic diseases(20 questions),conjunctivitis disease(20 questions),trachoma,pterygoid and conjunctival tumor diseases(20 questions),and dry eye disease(20 questions).Then the total score of each LLMs and compared their mean score,mean correlation,variance,and confidence were calculated.RESULTS:GPT-4 exhibited the highest performance in terms of LLMs.Comparing the average scores of the LLMs group with the four human groups,chief physician,attending physician,regular trainee,and graduate student,it was found that except for ChatGPT-4,the total score of the rest of the LLMs is lower than that of the graduate student group,which had the lowest score in the human group.Both ChatGPT-4 and PaLM2 were more likely to give exact and correct answers,giving very little chance of an incorrect answer.ChatGPT-4 showed higher credibility when answering questions,with a success rate of 59%,but gave the wrong answer to the question 28% of the time.CONCLUSION:GPT-4 model exhibits excellent performance in both answer relevance and confidence.PaLM2 shows a positive correlation(up to 0.8)in terms of answer accuracy during the exam.In terms of answer confidence,PaLM2 is second only to GPT4 and surpasses Claude 2,SenseNova,and GPT-3.5.Despite the fact that ocular surface disease is a highly specialized discipline,GPT-4 still exhibits superior performance,suggesting that its potential and ability to be applied in this field is enormous,perhaps with the potential to be a valuable resource for medical students and clinicians in the future.
文摘The rapid advancement of Artificial Intelligence(AI)and Large Language Models(LLMs)has led to their increasing integration into various domains,from text generation and translation to question-answering.However,a critical question remains:do these sophisticated models,much like humans,exhibit susceptibility to cognitive biases?Understanding the presence and nature of such biases in AI is paramount for assessing their reliability,enhancing their performance,and predicting their societal impact.This research specifically investigates the susceptibility of Google’s Gemini 1.5 Pro and DeepSeek,two prominent LLMs,to framing effects and confirmation bias.The study meticulously designed a series of experimental trials,systematically manipulating information proportions and presentation orders to evaluate these biases.In the framing effect experiment,a genetic testing decision-making scenario was constructed.The proportion of positive and negative information(e.g.,20%,50%,or 80%positive)and their presentation order were varied.The models’inclination towards undergoing genetic testing was recorded.For the confirmation bias experiment,two reports-one positive and one negative-about“RoboTaxi”autonomous vehicles were provided.The proportion of erroneous information within these reports(10%,30%,and 50%)and their presentation order were systematically altered,and the models’support for each report was assessed.The findings demonstrate that both Gemini 1.5 Pro and DeepSeek are susceptible to framing effects.In the genetic testing scenario,their decision-making was primarily influenced by the proportion of positive and negative information presented.When the proportion of positive information was higher,both models showed a greater inclination to recommend or proceed with genetic testing.Conversely,a higher proportion of negative information led to greater caution or a tendency not to recommend the testing.Importantly,the order in which this information was presented did not significantly influence their decisions in the framing effect scenarios.Regarding confirmation bias,the two models exhibited distinct behaviors.Gemini 1.5 Pro did not show an overall preference for either positive or negative reports.However,its judgments were significantly influenced by the order of information presentation,demonstrating a“recency effect,”meaning it tended to support the report presented later.The proportion of erroneous information within the reports had no significant impact on Gemini 1.5 Pro’s decisions.In contrast,DeepSeek exhibited an overall confirmation bias,showing a clear preference for positive reports.Similar to Gemini 1.5 Pro,DeepSeek’s decisions were also significantly affected by the order of information presentation,while the proportion of misinformation had no significant effect.These results reveal human-like cognitive vulnerabilities in advanced LLMs,highlighting critical challenges to their reliability and objectivity in decision-making processes.Gemini 1.5 Pro’s sensitivity to presentation order and DeepSeek’s general preference for positive information,coupled with its sensitivity to order,underscore the need for careful evaluation of potential cognitive biases during the development and application of AI.The study suggests that effective measures are necessary to mitigate these biases and prevent potential negative societal impacts.Future research should include a broader range of models for comparative analysis and explore more complex interactive scenarios to further understand and address these phenomena.The findings contribute significantly to understanding the limitations and capabilities of current AI systems,guiding their responsible development,and anticipating their potential societal implications.
文摘Smart contracts on the Ethereum blockchain continue to revolutionize decentralized applications (dApps) by allowing for self-executing agreements. However, bad actors have continuously found ways to exploit smart contracts for personal financial gain, which undermines the integrity of the Ethereum blockchain. This paper proposes a computer program called SADA (Static and Dynamic Analyzer), a novel approach to smart contract vulnerability detection using multiple Large Language Model (LLM) agents to analyze and flag suspicious Solidity code for Ethereum smart contracts. SADA not only improves upon existing vulnerability detection methods but also paves the way for more secure smart contract development practices in the rapidly evolving blockchain ecosystem.
文摘AIM:To investigate the capabilities of large language models(LLM)for providing information and diagnoses in the field of neuro-ophthalmology by comparing the performances of ChatGPT-3.5 and-4.0,Bard,and Bing.METHODS:Each chatbot was evaluated for four criteria,namely diagnostic success rate for the described case,answer quality,response speed,and critical keywords for diagnosis.The selected topics included optic neuritis,nonarteritic anterior ischemic optic neuropathy,and Leber hereditary optic neuropathy.RESULTS:In terms of diagnostic success rate for the described cases,Bard was unable to provide a diagnosis.The success rates for the described cases increased in the order of Bing,ChatGPT-3.5,and ChatGPT-4.0.Further,ChatGPT-4.0 and-3.5 provided the most satisfactory answer quality for judgment by neuro-ophthalmologists,with their sets of answers resembling the sample set most.Bard was only able to provide ten differential diagnoses in three trials.Bing scored the lowest for the satisfactory standard.A Mann-Whitney test indicated that Bard was significantly faster than ChatGPT-4.0(Z=-3.576,P=0.000),ChatGPT-3.5(Z=-3.576,P=0.000)and Bing(Z=-2.517,P=0.011).ChatGPT-3.5 and-4.0 far exceeded the other two interfaces at providing diagnoses and were thus used to find the critical keywords for diagnosis.CONCLUSION:ChatGPT-3.5 and-4.0 are better than Bard and Bing in terms of answer success rate,answer quality,and critical keywords for diagnosis in ophthalmology.This study has broad implications for the field of ophthalmology,providing further evidence that artificial intelligence LLM can aid clinical decision-making through free-text explanations.
基金supported supported by the Fundamental Research Funds for the Central Universities(226-2024-00004)the National Natural Science Foundation of China(U23 A20326)Key Research and Development Program of Zhejiang Province(2025C01061).
文摘Dear Editor,This letter deals with automatically constructing an OPC UA information model(IM)aimed at enhancing data interoperability among heterogeneous system components within manufacturing automation systems.Empowered by the large language model(LLM),we propose a novel multi-agent collaborative framework to streamline the end-to-end OPC UA IM modeling process.Each agent is equipped with meticulously engineered prompt templates,augmenting their capacity to execute specific tasks.We conduct modeling experiments using real textual data to demonstrate the effectiveness of the proposed method,improving modeling efficiency and reducing the labor workload.
文摘As large language models increasingly permeate medical workflows,a recent study evaluating ChatGPT 4.0’s performance in addressing patient queries about endoscopic submucosal dissection and endoscopic mucosal resection offers critical insights into three domains:Performance parity,cost democratization,and clinical readiness.The findings highlight ChatGPT’s high accuracy,completeness,and comprehensibility,suggesting potential cost efficiency in patient education.Yet,cost-effectiveness alone does not ensure clinical utility.Notably,the study relied exclusively on text-based prompts,omitting multimodal data such as photographs or endoscopic scans.This is a significant limitation in a visually driven field like endoscopy,where large language model performance may drop precipitously without image context.Without multimodal integration,artificial intelligence tools risk failing to capture key diagnostic signals,underscoring the need for cautious adoption and robust validation in clinical practice.
文摘Preserving formal style in neural machine translation (NMT) is essential, yet often overlooked as an optimization objective of the training processes. This oversight can lead to translations that, though accurate, lack formality. In this paper, we propose how to improve NMT formality with large language models (LLMs), which combines the style transfer and evaluation capabilities of an LLM and the high-quality translation generation ability of NMT models to improve NMT formality. The proposed method (namely INMTF) encompasses two approaches. The first involves a revision approach using an LLM to revise the NMT-generated translation, ensuring a formal translation style. The second approach employs an LLM as a reward model for scoring translation formality, and then uses reinforcement learning algorithms to fine-tune the NMT model to maximize the reward score, thereby enhancing the formality of the generated translations. Considering the substantial parameter size of LLMs, we also explore methods to reduce the computational cost of INMTF. Experimental results demonstrate that INMTF significantly outperforms baselines in terms of translation formality and translation quality, with an improvement of +9.19 style accuracy points in the German-to-English task and +2.16 COMET score in the Russian-to-English task. Furthermore, our work demonstrates the potential of integrating LLMs within NMT frameworks to bridge the gap between NMT outputs and the formality required in various real-world translation scenarios.
基金supported by the National Natural Science Foundation of China(Grant No.:62101087)the China Postdoctoral Science Foundation(Grant No.:2021MD703942)+2 种基金the Chongqing Postdoctoral Research Project Special Funding,China(Grant No.:2021XM2016)the Science Foundation of Chongqing Municipal Commission of Education,China(Grant No.:KJQN202100642)the Chongqing Natural Science Foundation,China(Grant No.:cstc2021jcyj-msxmX0834).
文摘Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches often rely on limited data sources and simplistic hypotheses,which restrict their ability to capture the multi-faceted nature of biological systems.This study introduces adaptive multi-view learning(AMVL),a novel methodology that integrates chemical-induced transcriptional profiles(CTPs),knowledge graph(KG)embeddings,and large language model(LLM)representations,to enhance drug repurposing predictions.AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning(MVL),matrix factorization,and ensemble optimization techniques to integrate heterogeneous multi-source data.Comprehensive evaluations on benchmark datasets(Fdata-set,Cdataset,and Ydataset)and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art(SOTA)methods,achieving superior accuracy in predicting drug-disease associations across multiple metrics.Literature-based validation further confirmed the model's predictive capabilities,with seven out of the top ten predictions corroborated by post-2011 evidence.To promote transparency and reproducibility,all data and codes used in this study were open-sourced,providing resources for pro-cessing CTPs,KG,and LLM-based similarity calculations,along with the complete AMVL algorithm and benchmarking procedures.By unifying diverse data modalities,AMVL offers a robust and scalable so-lution for accelerating drug discovery,fostering advancements in translational medicine and integrating multi-omics data.We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.
基金funded by the National Natural Science Foundation of China(72104224,L2424237,71974107,L2224059,L2124002,and 91646102)the Beijing Natural Science Foundation(9232015)+4 种基金the Beijing Social Science Foundation(24GLC058)the Construction Project of China Knowledge Center for Engineering Sciences and Technology(CKCEST-2023-1-7)the MOE(Ministry of Education in China)Project of Humanities and Social Sciences(16JDGC011)the Tsinghua University Initiative Scientific Research Program(2019Z02CAU)the Tsinghua University Project of Volvo-Supported Green Economy and Sustainable Development(20183910020)。
文摘As large language models(LLMs)continue to demonstrate their potential in handling complex tasks,their value in knowledge-intensive industrial scenarios is becoming increasingly evident.Fault diagnosis,a critical domain in the industrial sector,has long faced the dual challenges of managing vast amounts of experiential knowledge and improving human-machine collaboration efficiency.Traditional fault diagnosis systems,which are primarily based on expert systems,suffer from three major limitations:(1)ineffective organization of fault diagnosis knowledge,(2)lack of adaptability between static knowledge frameworks and dynamic engineering environments,and(3)difficulties in integrating expert knowledge with real-time data streams.These systemic shortcomings restrict the ability of conventional approaches to handle uncertainty.In this study,we proposed an intelligent computer numerical control(CNC)fault diagnosis system,integrating LLMs with knowledge graph(KG).First,we constructed a comprehensive KG that consolidated multi-source data for structured representation.Second,we designed a retrievalaugmented generation(RAG)framework leveraging the KG to support multi-turn interactive fault diagnosis while incorporating real-time engineering data into the decision-making process.Finally,we introduced a learning mechanism to facilitate dynamic knowledge updates.The experimental results demonstrated that our system significantly improved fault diagnosis accuracy,outperforming engineers with two years of professional experience on our constructed benchmark datasets.By integrating LLMs and KG,our framework surpassed the limitations of traditional expert systems rooted in symbolic reasoning,offering a novel approach to addressing the cognitive paradox of unstructured knowledge modeling and dynamic environment adaptation in industrial settings.
