Anxiety,motivation,and strategy have long been seen as critical in second language acquisition.This study presents a systematic review of the literature on these variables in terms of their relationship with one anoth...Anxiety,motivation,and strategy have long been seen as critical in second language acquisition.This study presents a systematic review of the literature on these variables in terms of their relationship with one another,their effects on learning outcomes,and how they are affected by technology-assisted tools in the teaching of Chinese as a second language.This study includes 24 articles for the review study based on the criteria and process of the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol(PRISMA-P)and the clustering techniques of VOSviewer.It is found that 1)anxiety,motivation,and strategy were interrelated,that is,motivation was negatively associated with anxiety but positively related to strategy,while strategy could positively predict anxiety;2)anxiety could both positively and negatively affect learning outcomes,while motivation and strategy could both positively and insignificantly influence learning outcomes;3)the technology-assisted tools used in the classroom could both positively and negatively affect the levels of these variables and learning outcomes in the L2 Chinese context.The need to explore more complicated relationships between language-specific individual variables themselves and other possible factors that affect these variables,such as cultural ones,are also discussed for future research.展开更多
Recently,diffusion models have emerged as a promising paradigm for molecular design and optimization.However,most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geom-etries,with limited ...Recently,diffusion models have emerged as a promising paradigm for molecular design and optimization.However,most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geom-etries,with limited research on molecular sequence diffusion models.The International Union of Pure and Applied Chemistry(IUPAC)names are more akin to chemical natural language than the simplified molecular input line entry system(SMILES)for organic compounds.In this work,we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language(SMILES)and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language.We propose DiffIUPAC,a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings.Evaluation results demonstrate that our model out-performs existing methods and successfully captures the semantic rules of both chemical languages.Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints.Additionally,to illustrate the model’s applicability in drug design,we conducted case studies in functional group editing,analogue design and linker design.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
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
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
Sign language dataset is essential in sign language recognition and translation(SLRT). Current public sign language datasets are small and lack diversity, which does not meet the practical application requirements for...Sign language dataset is essential in sign language recognition and translation(SLRT). Current public sign language datasets are small and lack diversity, which does not meet the practical application requirements for SLRT. However, making a large-scale and diverse sign language dataset is difficult as sign language data on the Internet is scarce. In making a large-scale and diverse sign language dataset, some sign language data qualities are not up to standard. This paper proposes a two information streams transformer(TIST) model to judge whether the quality of sign language data is qualified. To verify that TIST effectively improves sign language recognition(SLR), we make two datasets, the screened dataset and the unscreened dataset. In this experiment, this paper uses visual alignment constraint(VAC) as the baseline model. The experimental results show that the screened dataset can achieve better word error rate(WER) than the unscreened dataset.展开更多
We analyze the suitability of existing pre-trained transformer-based language models(PLMs)for abstractive text summarization on German technical healthcare texts.The study focuses on the multilingual capabilities of t...We analyze the suitability of existing pre-trained transformer-based language models(PLMs)for abstractive text summarization on German technical healthcare texts.The study focuses on the multilingual capabilities of these models and their ability to perform the task of abstractive text summarization in the healthcare field.The research hypothesis was that large language models could perform high-quality abstractive text summarization on German technical healthcare texts,even if the model is not specifically trained in that language.Through experiments,the research questions explore the performance of transformer language models in dealing with complex syntax constructs,the difference in performance between models trained in English and German,and the impact of translating the source text to English before conducting the summarization.We conducted an evaluation of four PLMs(GPT-3,a translation-based approach also utilizing GPT-3,a German language Model,and a domain-specific bio-medical model approach).The evaluation considered the informativeness using 3 types of metrics based on Recall-Oriented Understudy for Gisting Evaluation(ROUGE)and the quality of results which is manually evaluated considering 5 aspects.The results show that text summarization models could be used in the German healthcare domain and that domain-independent language models achieved the best results.The study proves that text summarization models can simplify the search for pre-existing German knowledge in various domains.展开更多
This mixed-methods study presents a needs analysis to investigate the workplace English language needs of medical students in China who are learning and using English as non-native speakers,the circumstances in which ...This mixed-methods study presents a needs analysis to investigate the workplace English language needs of medical students in China who are learning and using English as non-native speakers,the circumstances in which the various language skills are required,and stakeholders’perceived workplace preparedness in the light of language-related instructional provision during medical training.A leading university in China was chosen as the study case.Altogether,294 online questionnaires were collected from undergraduate medical students,graduate medical students and recent graduates working as physicians,and 33 semi-structured individual interviews were conducted with undergraduate medical students,graduate medical students,recent graduates working as physicians,medical teachers,English for Medical Purposes(EMP)teachers,program leaders and English-speaking patients.Results showed that in addition to physicians experiencing pressure to publish scientific articles internationally,participants attached greater importance to physicians’oral English communication ability,especially in undertaking clinical consultations in English,working with medical interpreters or acting as ad hoc interpreters.The participants also reported a lack of relevant EMP courses or trainings available at this university.Given these communicative events that physicians face in China,EMP courses need to include training in these specific areas.展开更多
This paper examines the application of drama-based pedagogy in EFL classrooms,demonstrating how script analysis,role-playing,and improvisation can effectively enhance students’integrated language skills.The study hig...This paper examines the application of drama-based pedagogy in EFL classrooms,demonstrating how script analysis,role-playing,and improvisation can effectively enhance students’integrated language skills.The study highlights the unique advantages of dramatic texts for pronunciation training,subtext interpretation,and cultural understanding,while providing practical teaching methods including conflict scene selection and stage direction adaptation.Findings indicate that drama techniques reduce learning anxiety,boost motivation,and create authentic language contexts,serving as an effective bridge between literary study and language practice.展开更多
Background:Foreign Language Anxiety(FLA)represents a substantial affective barrier that undermines cognitive performance,motivation,and retention in language learners.Emerging evidence highlights mindfulness-based int...Background:Foreign Language Anxiety(FLA)represents a substantial affective barrier that undermines cognitive performance,motivation,and retention in language learners.Emerging evidence highlights mindfulness-based interventions as promising strategies for enhancing emotional regulation and reducing anxiety across educational contexts.This review synthesizes current research on mindfulness as a psychological intervention,aims to evaluate its efficacy in alleviating FLA,and discusses its broader implications for health-focused educational policy and practice.Methods:Following PRISMA guidelines,we systematically reviewed studies examining the relationships between mindfulness and FLA.Our search of four major databases(November 2023)initially identified 346 articles using terms like“mindfulness AND language anxiety.”After screening,14 studies met our criteria:(1)empirical research in English on mindfulness-FLA relationships;(2)no publication date restrictions.Two independent reviewers selected studies,excluding two due to methodological limitations.We conducted a narrative synthesis given the study heterogeneity(9 correlational and 5 intervention studies).Results:9 non-intervention studies demonstrated that mindfulness is negatively associated with FLA,with 3 studies highlighting the mediating roles of self-efficacy and resilience.5 intervention studies reported inconsistent results regarding the efficacy of mindfulness-based interventions in reducing FLA.Conclusions:The findings suggest that while mindfulness holds promise as a tool to address FLA,its mechanisms and effectiveness require further investigation.This study underscores the need for rigorous research,including Randomized Controlled Trials(RCTs),to inform evidence-based integration of mindfulness into foreign language curricula.For educational policymakers and practitioners,these insights highlight the importance of adopting mindfulness interventions cautiously,ensuring they are tailored to students’needs and supported by evidence.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
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
With the deepening of cross-cultural educational cooperation between China and Malaysia,the cross-cultural challenges that Chinese overseas students face in Malaysia due to language and cultural differences have becom...With the deepening of cross-cultural educational cooperation between China and Malaysia,the cross-cultural challenges that Chinese overseas students face in Malaysia due to language and cultural differences have become increasingly prominent.Focusing on Chinese graduate students at a public university in Malaysia where English is the medium of instruction,this study employs a scale survey method in conjunction with IBM SPSS 26.0 and Smart PLS 4.0 for data analysis to quantitatively explore the level of language anxiety and its relationship with cross-cultural adaptability and learning motivation.The results indicate that most Chinese graduate students experience notable language anxiety,which is significantly negatively correlated with cross-cultural adaptability,especially academic adaptability,but is not related to learning motivation.Furthermore,the study reveals the complex influencing mechanism of language anxiety within multicultural educational environments and offers suggestions for improvement tailored to Malaysia’s unique educational context.These include utilizing technological tools for language interventions,optimizing classroom teaching strategies,enhancing language learning motivation through external incentives,strengthening training for cross-cultural adaptation skills,and promoting deeper cross-cultural communication.This study provides theoretical support and practical references for alleviating language anxiety and enhancing the cross-cultural adaptability of Chinese overseas students.展开更多
Data-driven approaches are extensively employed to model complex chemical engineering processes, such as hydrotreating, to address the challenges of mechanism-based methods demanding deep process understanding. Howeve...Data-driven approaches are extensively employed to model complex chemical engineering processes, such as hydrotreating, to address the challenges of mechanism-based methods demanding deep process understanding. However, the development of such models requires specialized expertise in data science, limiting their broader application. Large language models (LLMs), such as GPT-4, have demonstrated potential in supporting and guiding research efforts. This work presents a novel AI-assisted framework where GPT-4, through well-engineered prompts, facilitates the construction and explanation of multi-objective neural networks. These models predict hydrotreating products properties (such as distillation range), including refined diesel and refined gas oil, using feedstock properties, operating conditions, and recycle hydrogen composition. Gradient-weighted class activation mapping was employed to identify key features influencing the output variables. This work illustrates an innovative AI-guided paradigm for chemical engineering applications, and the designed prompts hold promise for adaptation to other complex processes.展开更多
This study explores the application of Production-Oriented Approach(POA)in teaching German to nursing students in colleges and universities.Focusing on the requirements of the German B1 exam,the study takes the themat...This study explores the application of Production-Oriented Approach(POA)in teaching German to nursing students in colleges and universities.Focusing on the requirements of the German B1 exam,the study takes the thematic unit of“Movement”as an example,and designs the teaching through the links of“drive-enable-evaluate”.The results show that POA effectively improves students’speaking and writing output,but there are still deficiencies in reading and listening.Accordingly,this study has proposed the strategies of“vocabulary in-depth facilitation”and“accurate matching of listening and writing”to optimize the input materials and skills training,achieve the goal of“learning and using”,and provide a practical path for teaching German for Special Purposes.This provides a practical path for the teaching of German for special purposes.展开更多
As a common foodborne pathogen,Salmonella poses risks to public health safety,common given the emergence of antimicrobial-resistant strains.However,there is currently a lack of systematic platforms based on large lang...As a common foodborne pathogen,Salmonella poses risks to public health safety,common given the emergence of antimicrobial-resistant strains.However,there is currently a lack of systematic platforms based on large language models(LLMs)for Salmonella resistance prediction,data presentation,and data sharing.To overcome this issue,we firstly propose a two-step feature-selection process based on the chi-square test and conditional mutual information maximization to find the key Salmonella resistance genes in a pan-genomics analysis and develop an LLM-based Salmonella antimicrobial-resistance predictive(SARPLLM)algorithm to achieve accurate antimicrobial-resistance prediction,based on Qwen2 LLM and low-rank adaptation.Secondly,we optimize the time complexity to compute the sample distance from the linear to logarithmic level by constructing a quantum data augmentation algorithm denoted as QSMOTEN.Thirdly,we build up a user-friendly Salmonella antimicrobial-resistance predictive online platform based on knowledge graphs,which not only facilitates online resistance prediction for users but also visualizes the pan-genomics analysis results of the Salmonella datasets.展开更多
Under the tide of economic globalization,college English teaching should not only focus on the improvement of language ability,but also on the cultivation of students’critical thinking ability.This paper takes the in...Under the tide of economic globalization,college English teaching should not only focus on the improvement of language ability,but also on the cultivation of students’critical thinking ability.This paper takes the integration of language learning and critical thinking ability as the breakthrough point,explores the college English teaching mode under the background of the integration of the two,analyzes the current situation and disadvantages of the separation of the two in the current teaching,and puts forward the integration path from the aspects of curriculum design,teacher training,evaluation system,and so on.With the help of activities such as creating real language situations,carrying out debates and critical reading,it helps students strengthen the improvement of logical analysis and critical thinking ability in their gradual learning,realize the coordinated development of language learning and critical thinking ability,and cultivate compound talents with both language literacy and critical thinking ability for the society.展开更多
Based on conversation analysis theory,this study explored how teachers and students used discourse markers in foreign language classrooms and how these markers affect classroom interaction.The results showed that teac...Based on conversation analysis theory,this study explored how teachers and students used discourse markers in foreign language classrooms and how these markers affect classroom interaction.The results showed that teachers use different discourse markers in different teaching stages such as lead-in,presentation,practice and summary to make their teaching more organized and clearer.However,students used these markers less often,and their expressions are often less coherent.Based on these findings,the study suggests teaching strategies for teachers and learning strategies for students to improve how they use discourse markers.This can help make foreign language teaching more effective and interactive.展开更多
文摘Anxiety,motivation,and strategy have long been seen as critical in second language acquisition.This study presents a systematic review of the literature on these variables in terms of their relationship with one another,their effects on learning outcomes,and how they are affected by technology-assisted tools in the teaching of Chinese as a second language.This study includes 24 articles for the review study based on the criteria and process of the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol(PRISMA-P)and the clustering techniques of VOSviewer.It is found that 1)anxiety,motivation,and strategy were interrelated,that is,motivation was negatively associated with anxiety but positively related to strategy,while strategy could positively predict anxiety;2)anxiety could both positively and negatively affect learning outcomes,while motivation and strategy could both positively and insignificantly influence learning outcomes;3)the technology-assisted tools used in the classroom could both positively and negatively affect the levels of these variables and learning outcomes in the L2 Chinese context.The need to explore more complicated relationships between language-specific individual variables themselves and other possible factors that affect these variables,such as cultural ones,are also discussed for future research.
基金supported by the Yonsei University graduate school Department of Integrative Biotechnology.
文摘Recently,diffusion models have emerged as a promising paradigm for molecular design and optimization.However,most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geom-etries,with limited research on molecular sequence diffusion models.The International Union of Pure and Applied Chemistry(IUPAC)names are more akin to chemical natural language than the simplified molecular input line entry system(SMILES)for organic compounds.In this work,we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language(SMILES)and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language.We propose DiffIUPAC,a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings.Evaluation results demonstrate that our model out-performs existing methods and successfully captures the semantic rules of both chemical languages.Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints.Additionally,to illustrate the model’s applicability in drug design,we conducted case studies in functional group editing,analogue design and linker design.
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.
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.
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.
基金supported by the National Language Commission to research on sign language data specifications for artificial intelligence applications and test standards for language service translation systems (No.ZDI145-70)。
文摘Sign language dataset is essential in sign language recognition and translation(SLRT). Current public sign language datasets are small and lack diversity, which does not meet the practical application requirements for SLRT. However, making a large-scale and diverse sign language dataset is difficult as sign language data on the Internet is scarce. In making a large-scale and diverse sign language dataset, some sign language data qualities are not up to standard. This paper proposes a two information streams transformer(TIST) model to judge whether the quality of sign language data is qualified. To verify that TIST effectively improves sign language recognition(SLR), we make two datasets, the screened dataset and the unscreened dataset. In this experiment, this paper uses visual alignment constraint(VAC) as the baseline model. The experimental results show that the screened dataset can achieve better word error rate(WER) than the unscreened dataset.
文摘We analyze the suitability of existing pre-trained transformer-based language models(PLMs)for abstractive text summarization on German technical healthcare texts.The study focuses on the multilingual capabilities of these models and their ability to perform the task of abstractive text summarization in the healthcare field.The research hypothesis was that large language models could perform high-quality abstractive text summarization on German technical healthcare texts,even if the model is not specifically trained in that language.Through experiments,the research questions explore the performance of transformer language models in dealing with complex syntax constructs,the difference in performance between models trained in English and German,and the impact of translating the source text to English before conducting the summarization.We conducted an evaluation of four PLMs(GPT-3,a translation-based approach also utilizing GPT-3,a German language Model,and a domain-specific bio-medical model approach).The evaluation considered the informativeness using 3 types of metrics based on Recall-Oriented Understudy for Gisting Evaluation(ROUGE)and the quality of results which is manually evaluated considering 5 aspects.The results show that text summarization models could be used in the German healthcare domain and that domain-independent language models achieved the best results.The study proves that text summarization models can simplify the search for pre-existing German knowledge in various domains.
文摘This mixed-methods study presents a needs analysis to investigate the workplace English language needs of medical students in China who are learning and using English as non-native speakers,the circumstances in which the various language skills are required,and stakeholders’perceived workplace preparedness in the light of language-related instructional provision during medical training.A leading university in China was chosen as the study case.Altogether,294 online questionnaires were collected from undergraduate medical students,graduate medical students and recent graduates working as physicians,and 33 semi-structured individual interviews were conducted with undergraduate medical students,graduate medical students,recent graduates working as physicians,medical teachers,English for Medical Purposes(EMP)teachers,program leaders and English-speaking patients.Results showed that in addition to physicians experiencing pressure to publish scientific articles internationally,participants attached greater importance to physicians’oral English communication ability,especially in undertaking clinical consultations in English,working with medical interpreters or acting as ad hoc interpreters.The participants also reported a lack of relevant EMP courses or trainings available at this university.Given these communicative events that physicians face in China,EMP courses need to include training in these specific areas.
文摘This paper examines the application of drama-based pedagogy in EFL classrooms,demonstrating how script analysis,role-playing,and improvisation can effectively enhance students’integrated language skills.The study highlights the unique advantages of dramatic texts for pronunciation training,subtext interpretation,and cultural understanding,while providing practical teaching methods including conflict scene selection and stage direction adaptation.Findings indicate that drama techniques reduce learning anxiety,boost motivation,and create authentic language contexts,serving as an effective bridge between literary study and language practice.
文摘Background:Foreign Language Anxiety(FLA)represents a substantial affective barrier that undermines cognitive performance,motivation,and retention in language learners.Emerging evidence highlights mindfulness-based interventions as promising strategies for enhancing emotional regulation and reducing anxiety across educational contexts.This review synthesizes current research on mindfulness as a psychological intervention,aims to evaluate its efficacy in alleviating FLA,and discusses its broader implications for health-focused educational policy and practice.Methods:Following PRISMA guidelines,we systematically reviewed studies examining the relationships between mindfulness and FLA.Our search of four major databases(November 2023)initially identified 346 articles using terms like“mindfulness AND language anxiety.”After screening,14 studies met our criteria:(1)empirical research in English on mindfulness-FLA relationships;(2)no publication date restrictions.Two independent reviewers selected studies,excluding two due to methodological limitations.We conducted a narrative synthesis given the study heterogeneity(9 correlational and 5 intervention studies).Results:9 non-intervention studies demonstrated that mindfulness is negatively associated with FLA,with 3 studies highlighting the mediating roles of self-efficacy and resilience.5 intervention studies reported inconsistent results regarding the efficacy of mindfulness-based interventions in reducing FLA.Conclusions:The findings suggest that while mindfulness holds promise as a tool to address FLA,its mechanisms and effectiveness require further investigation.This study underscores the need for rigorous research,including Randomized Controlled Trials(RCTs),to inform evidence-based integration of mindfulness into foreign language curricula.For educational policymakers and practitioners,these insights highlight the importance of adopting mindfulness interventions cautiously,ensuring they are tailored to students’needs and supported by evidence.
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 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funded by the 2022 Annual Key Research Project on Theoretical and Practical Studies of Ideological and Political Education for University Students in GuangxiSpecial Focus on University Counselors:Exploration and Practice of a Cultivation Ecosystem for Cultivating Both Moral Character and Talent Through “One Virtue+Two Lines+Three Stages+Four Micro-Education Methods” for Ideological and Political Education in Universities from the Perspective of Peer Language Systems,Project No.:2022MSZ031
文摘With the deepening of cross-cultural educational cooperation between China and Malaysia,the cross-cultural challenges that Chinese overseas students face in Malaysia due to language and cultural differences have become increasingly prominent.Focusing on Chinese graduate students at a public university in Malaysia where English is the medium of instruction,this study employs a scale survey method in conjunction with IBM SPSS 26.0 and Smart PLS 4.0 for data analysis to quantitatively explore the level of language anxiety and its relationship with cross-cultural adaptability and learning motivation.The results indicate that most Chinese graduate students experience notable language anxiety,which is significantly negatively correlated with cross-cultural adaptability,especially academic adaptability,but is not related to learning motivation.Furthermore,the study reveals the complex influencing mechanism of language anxiety within multicultural educational environments and offers suggestions for improvement tailored to Malaysia’s unique educational context.These include utilizing technological tools for language interventions,optimizing classroom teaching strategies,enhancing language learning motivation through external incentives,strengthening training for cross-cultural adaptation skills,and promoting deeper cross-cultural communication.This study provides theoretical support and practical references for alleviating language anxiety and enhancing the cross-cultural adaptability of Chinese overseas students.
基金supported by the National Key Research and Development Program of China(2023YFA1507601)the National Natural Science Foundation of China(22278127,22378038)+2 种基金the Fundamental Research Funds for the Central Universities(2022ZFJH004)the Shanghai Pilot Program for Basic Research(22T01400100-18)the Natural Science Foundation of Liaoning Province,China(2024-MSBA-15).
文摘Data-driven approaches are extensively employed to model complex chemical engineering processes, such as hydrotreating, to address the challenges of mechanism-based methods demanding deep process understanding. However, the development of such models requires specialized expertise in data science, limiting their broader application. Large language models (LLMs), such as GPT-4, have demonstrated potential in supporting and guiding research efforts. This work presents a novel AI-assisted framework where GPT-4, through well-engineered prompts, facilitates the construction and explanation of multi-objective neural networks. These models predict hydrotreating products properties (such as distillation range), including refined diesel and refined gas oil, using feedstock properties, operating conditions, and recycle hydrogen composition. Gradient-weighted class activation mapping was employed to identify key features influencing the output variables. This work illustrates an innovative AI-guided paradigm for chemical engineering applications, and the designed prompts hold promise for adaptation to other complex processes.
文摘This study explores the application of Production-Oriented Approach(POA)in teaching German to nursing students in colleges and universities.Focusing on the requirements of the German B1 exam,the study takes the thematic unit of“Movement”as an example,and designs the teaching through the links of“drive-enable-evaluate”.The results show that POA effectively improves students’speaking and writing output,but there are still deficiencies in reading and listening.Accordingly,this study has proposed the strategies of“vocabulary in-depth facilitation”and“accurate matching of listening and writing”to optimize the input materials and skills training,achieve the goal of“learning and using”,and provide a practical path for teaching German for Special Purposes.This provides a practical path for the teaching of German for special purposes.
基金supported by the National Science and Technology Major Project(2021YFF1201200)the National Natural Science Foundation of China(62372316)the Sichuan Science and Technology Program key project(2024YFHZ0091).
文摘As a common foodborne pathogen,Salmonella poses risks to public health safety,common given the emergence of antimicrobial-resistant strains.However,there is currently a lack of systematic platforms based on large language models(LLMs)for Salmonella resistance prediction,data presentation,and data sharing.To overcome this issue,we firstly propose a two-step feature-selection process based on the chi-square test and conditional mutual information maximization to find the key Salmonella resistance genes in a pan-genomics analysis and develop an LLM-based Salmonella antimicrobial-resistance predictive(SARPLLM)algorithm to achieve accurate antimicrobial-resistance prediction,based on Qwen2 LLM and low-rank adaptation.Secondly,we optimize the time complexity to compute the sample distance from the linear to logarithmic level by constructing a quantum data augmentation algorithm denoted as QSMOTEN.Thirdly,we build up a user-friendly Salmonella antimicrobial-resistance predictive online platform based on knowledge graphs,which not only facilitates online resistance prediction for users but also visualizes the pan-genomics analysis results of the Salmonella datasets.
文摘Under the tide of economic globalization,college English teaching should not only focus on the improvement of language ability,but also on the cultivation of students’critical thinking ability.This paper takes the integration of language learning and critical thinking ability as the breakthrough point,explores the college English teaching mode under the background of the integration of the two,analyzes the current situation and disadvantages of the separation of the two in the current teaching,and puts forward the integration path from the aspects of curriculum design,teacher training,evaluation system,and so on.With the help of activities such as creating real language situations,carrying out debates and critical reading,it helps students strengthen the improvement of logical analysis and critical thinking ability in their gradual learning,realize the coordinated development of language learning and critical thinking ability,and cultivate compound talents with both language literacy and critical thinking ability for the society.
基金Survey on the Use of Discourse Markers by Teachers and Students in Foreign Language Classrooms:Taking Teaching Stages and Contexts as Examples(Project No.:20240847)Ideological and Political Teaching Practice of Linguistics Course Under New Liberal Arts(Project No.:JG2023074)。
文摘Based on conversation analysis theory,this study explored how teachers and students used discourse markers in foreign language classrooms and how these markers affect classroom interaction.The results showed that teachers use different discourse markers in different teaching stages such as lead-in,presentation,practice and summary to make their teaching more organized and clearer.However,students used these markers less often,and their expressions are often less coherent.Based on these findings,the study suggests teaching strategies for teachers and learning strategies for students to improve how they use discourse markers.This can help make foreign language teaching more effective and interactive.
