Objective: Smoking cessation during pregnancy is a modifiable intervention that can improve maternal and neonatal outcomes. Encouraging smoking cessation is an assessed measure of the Meaningful Use incentives to ensu...Objective: Smoking cessation during pregnancy is a modifiable intervention that can improve maternal and neonatal outcomes. Encouraging smoking cessation is an assessed measure of the Meaningful Use incentives to ensure best practices with the increased use of the electronic medical record (EMR). Physician EMR prompts have been used shown to be successful with preventive care but there is a paucity of data evaluating prompts within obstetrics. The objective of this study is to determine the effectiveness of enhanced smoking cessation prompts in a prenatal EMR. Methods: A retrospective cohort study of an enhanced smoking cessation prompting system within our prenatal EMR was performed. Pregnant women who reported tobacco use at first prenatal visit were included. The number of times a smoking cessation method was offered and documented, the number of documented attempts at smoking cessation, and the final number of cigarettes smoked were compared pre and post the enhancement of the smoking cessation prompting system. Results: 95 patients were included (48 pre-enhancement;47 post-enhancement). Post-enhancement, the documentation of smoking cessation method offered increased (0 vs. 1, p = 0.03) and documentation of smoking cessation attempts increased (1 vs. 2, p = 0.006). There was no change in the final number of cigarettes smoked (p = 0.9). Conclusions: Enhanced prompting systems increase documentation related to smoking cessation with no change in number of cigarettes smoked. In the era of Meaningful Use guidelines which focus on documentation in the EMR, continued research must be done to assure that software enhancements and improved documentation truly result in improved patient care.展开更多
Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural ...Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural artifacts online.As an effective medium,posters serve to attract public attention and facilitate broader engagement with cultural artifacts.However,existing poster generation methods mainly rely on fixed templates and manual design,which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts.Therefore,we propose CAPGen,an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language Model(MLLM)with integrated iterative optimization.During our research,we collaborated with designers to define principles of graphic design for cultural artifact posters,to guide the MLLM in generating layout parameters.Later,we generated these parameters into posters.Finally,we refined the posters using an MLLM integrated with a multi-round iterative optimization mechanism.Qualitative results show that CAPGen consistently outperforms baseline methods in both visual quality and aesthetic performance.Furthermore,ablation studies indicate that the prompt,iterative optimization mechanism,and design principles significantly enhance the effectiveness of poster generation.展开更多
Dear Editor,Chibanian(Middle Pleistocene)hominin fossils that could not be easily assigned to Homo erectus,H.neanderthalensis,or H.sapiens have traditionally been as-signed to an alinclusive group:"archaic H.sapi...Dear Editor,Chibanian(Middle Pleistocene)hominin fossils that could not be easily assigned to Homo erectus,H.neanderthalensis,or H.sapiens have traditionally been as-signed to an alinclusive group:"archaic H.sapiens."In an insightful observation of the Chibanian record almost four decades ago however,Tattersall railed against the use of the word"archaic"in this sense when referring to the human fossil record,as he justifiably noted that no other biological organism has the word"archaic"attached to it'For example,no one refers to an earlier version of Canis domesticus as"archaic"C.domesticus.The ancestor of the domestic dog is,and always has been,considered to be C.lupus.In Tattersall's opinion,it would seem that these"archaic H.sapiens"fossils should be assigned to one or more formal taxonomic names.As such,terms such as"archaic H.sapiens,""mid-Pleistocene Homo,"and"Middle Pleistocene Homo"have al-ways been considered to be wastebasket taxa that include way too much morphological variabilty for one proposed taxonomic group.Continuing to use wastebasket taxa only hinders any attempts to understand true phylogenetic and evolutionary relationships.展开更多
Image emotion classification(IEC)aims to extract the abstract emotions evoked in images.Recently,language-supervised methods such as con-trastive language-image pretraining(CLIP)have demonstrated superior performance ...Image emotion classification(IEC)aims to extract the abstract emotions evoked in images.Recently,language-supervised methods such as con-trastive language-image pretraining(CLIP)have demonstrated superior performance in image under-standing.However,the underexplored task of IEC presents three major challenges:a tremendous training objective gap between pretraining and IEC,shared suboptimal prompts,and invariant prompts for all instances.In this study,we propose a general framework that effectively exploits the language-supervised CLIP method for the IEC task.First,a prompt-tuning method that mimics the pretraining objective of CLIP is introduced,to exploit the rich image and text semantics associated with CLIP.Subsequently,instance-specific prompts are automatically composed,conditioning them on the categories and image content of instances,diversifying the prompts,and thus avoiding suboptimal problems.Evaluations on six widely used affective datasets show that the proposed method significantly outperforms state-of-the-art methods(up to 9.29%accuracy gain on the EmotionROI dataset)on IEC tasks with only a few trained parameters.The code is publicly available at https://github.com/dsn0w/PT-DPC/for research purposes.展开更多
ChatGPT has emerged as a promising advanced large language model that needs prompt to gain information.However,designing a good prompt is not an easy task for many end-users.Therefore,this study intends to determine t...ChatGPT has emerged as a promising advanced large language model that needs prompt to gain information.However,designing a good prompt is not an easy task for many end-users.Therefore,this study intends to determine the amount of information gained because of varied amounts of information in the prompt.This study used two types of prompts,initial and improved,to query the introduction sections of 327 highly cited articles on traffic safety.The queried introduction sections were then matched with the corresponding human-written introduction sections from the same articles.Similarity tests and text network analysis were used to understand the level of similarities and the content of ChatGPT-generated and human-written introductions.The findings indicate the improved prompts,which have the addition of generic persona and information about the citations and references,changed the ChatGPT's output insignificantly.While the perfect similar contents are supposed to have a 1.0 similarity score,the initial and improved prompt's introduction materials have average similarity scores of 0.5387 and 0.5567,respectively.Further,the content analysis revealed that themes such as statistics,trends,safety measures,and safety technologies are more likely to have high similarity scores,irrespective of the amount of information provided in the prompt.On the other hand,themes such as human behavior,policy and regulations,public perception,and emerging technologies require a detailed level of information in their prompt to produce materials that are close to human-written materials.The prompt engineers can use the findings to evaluate their outputs and improve their prompting skills.展开更多
Artificial intelligence(AI)is advancing swiftly and integrating into various societal domains,including international Chinese language teaching.While AI provides diverse advantages,it also presents inherent risks,akin...Artificial intelligence(AI)is advancing swiftly and integrating into various societal domains,including international Chinese language teaching.While AI provides diverse advantages,it also presents inherent risks,akin to a“double-edged sword”.This paper delves into the challenges and opportunities associated with AI and suggests a strategy to transform AI from a potential adversary to an ally:not avoidance,but mastery.Mastery entails crafting targeted prompts suitable for distinct contexts to attain desired AI-driven outcomes.Ultimately,by presenting case studies across various proficiency levels in Chinese language instruction that utilize AI tools,this paper aims to stimulate constructive dialogue within the academic community of Chinese language teaching.展开更多
Information extraction(IE)aims to automatically identify and extract information about specific interests from raw texts.Despite the abundance of solutions based on fine-tuning pretrained language models,IE in the con...Information extraction(IE)aims to automatically identify and extract information about specific interests from raw texts.Despite the abundance of solutions based on fine-tuning pretrained language models,IE in the context of fewshot and zero-shot scenarios remains highly challenging due to the scarcity of training data.Large language models(LLMs),on the other hand,can generalize well to unseen tasks with few-shot demonstrations or even zero-shot instructions and have demonstrated impressive ability for a wide range of natural language understanding or generation tasks.Nevertheless,it is unclear,whether such effectiveness can be replicated in the task of IE,where the target tasks involve specialized schema and quite abstractive entity or relation concepts.In this paper,we first examine the validity of LLMs in executing IE tasks with an established prompting strategy and further propose multiple types of augmented prompting methods,including the structured fundamental prompt(SFP),the structured interactive reasoning prompt(SIRP),and the voting-enabled structured interactive reasoning prompt(VESIRP).The experimental results demonstrate that while directly promotes inferior performance,the proposed augmented prompt methods significantly improve the extraction accuracy,achieving comparable or even better performance(e.g.,zero-shot FewNERD,FewNERD-INTRA)than state-of-theart methods that require large-scale training samples.This study represents a systematic exploration of employing instruction-following LLM for the task of IE.It not only establishes a performance benchmark for this novel paradigm but,more importantly,validates a practical technical pathway through the proposed prompt enhancement method,offering a viable solution for efficient IE in low-resource settings.展开更多
Large Language Models(LLMs)have significantly advanced human-computer interaction by improving natural language understanding and generation.However,their vulnerability to adversarial prompts–carefully designed input...Large Language Models(LLMs)have significantly advanced human-computer interaction by improving natural language understanding and generation.However,their vulnerability to adversarial prompts–carefully designed inputs that manipulate model outputs–presents substantial challenges.This paper introduces a classification-based approach to detect adversarial prompts by utilizing both prompt features and prompt response features.Elevenmachine learning models were evaluated based on key metrics such as accuracy,precision,recall,and F1-score.The results show that the Convolutional Neural Network–Long Short-Term Memory(CNN-LSTM)cascade model delivers the best performance,especially when using prompt features,achieving an accuracy of over 97%in all adversarial scenarios.Furthermore,the Support Vector Machine(SVM)model performed best with prompt response features,particularly excelling in prompt type classification tasks.Classification results revealed that certain types of adversarial attacks,such as“Word Level”and“Adversarial Prefix”,were particularly difficult to detect,as indicated by their low recall and F1-scores.These findings suggest that more subtle manipulations can evade detection mechanisms.In contrast,attacks like“Sentence Level”and“Adversarial Insertion”were easier to identify,due to the model’s effectiveness in recognizing inserted content.Natural Language Processing(NLP)techniques played a critical role by enabling the extraction of semantic and syntactic features from both prompts and their corresponding responses.These insights highlight the importance of combining traditional and deep learning approaches,along with advanced NLP techniques,to build more reliable adversarial prompt detection systems for LLMs.展开更多
The development of generative architectures has resulted in numerous novel deep-learning models that generate images using text inputs.However,humans naturally use speech for visualization prompts.Therefore,this paper...The development of generative architectures has resulted in numerous novel deep-learning models that generate images using text inputs.However,humans naturally use speech for visualization prompts.Therefore,this paper proposes an architecture that integrates speech prompts as input to image-generation Generative Adversarial Networks(GANs)model,leveraging Speech-to-Text translation along with the CLIP+VQGAN model.The proposed method involves translating speech prompts into text,which is then used by the Contrastive Language-Image Pretraining(CLIP)+Vector Quantized Generative Adversarial Network(VQGAN)model to generate images.This paper outlines the steps required to implement such a model and describes in detail the methods used for evaluating the model.The GAN model successfully generates artwork from descriptions using speech and text prompts.Experimental outcomes of synthesized images demonstrate that the proposed methodology can produce beautiful abstract visuals containing elements from the input prompts.The model achieved a Frechet Inception Distance(FID)score of 28.75,showcasing its capability to produce high-quality and diverse images.The proposed model can find numerous applications in educational,artistic,and design spaces due to its ability to generate images using speech and the distinct abstract artistry of the output images.This capability is demonstrated by giving the model out-of-the-box prompts to generate never-before-seen images with plausible realistic qualities.展开更多
文摘Objective: Smoking cessation during pregnancy is a modifiable intervention that can improve maternal and neonatal outcomes. Encouraging smoking cessation is an assessed measure of the Meaningful Use incentives to ensure best practices with the increased use of the electronic medical record (EMR). Physician EMR prompts have been used shown to be successful with preventive care but there is a paucity of data evaluating prompts within obstetrics. The objective of this study is to determine the effectiveness of enhanced smoking cessation prompts in a prenatal EMR. Methods: A retrospective cohort study of an enhanced smoking cessation prompting system within our prenatal EMR was performed. Pregnant women who reported tobacco use at first prenatal visit were included. The number of times a smoking cessation method was offered and documented, the number of documented attempts at smoking cessation, and the final number of cigarettes smoked were compared pre and post the enhancement of the smoking cessation prompting system. Results: 95 patients were included (48 pre-enhancement;47 post-enhancement). Post-enhancement, the documentation of smoking cessation method offered increased (0 vs. 1, p = 0.03) and documentation of smoking cessation attempts increased (1 vs. 2, p = 0.006). There was no change in the final number of cigarettes smoked (p = 0.9). Conclusions: Enhanced prompting systems increase documentation related to smoking cessation with no change in number of cigarettes smoked. In the era of Meaningful Use guidelines which focus on documentation in the EMR, continued research must be done to assure that software enhancements and improved documentation truly result in improved patient care.
基金supported by the National Key Research and Development Program of China(2023YFF0906502)the Postgraduate Research and Innovation Project of Hunan Province under Grant(CX20240473).
文摘Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural artifacts online.As an effective medium,posters serve to attract public attention and facilitate broader engagement with cultural artifacts.However,existing poster generation methods mainly rely on fixed templates and manual design,which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts.Therefore,we propose CAPGen,an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language Model(MLLM)with integrated iterative optimization.During our research,we collaborated with designers to define principles of graphic design for cultural artifact posters,to guide the MLLM in generating layout parameters.Later,we generated these parameters into posters.Finally,we refined the posters using an MLLM integrated with a multi-round iterative optimization mechanism.Qualitative results show that CAPGen consistently outperforms baseline methods in both visual quality and aesthetic performance.Furthermore,ablation studies indicate that the prompt,iterative optimization mechanism,and design principles significantly enhance the effectiveness of poster generation.
基金National Natural Science Foundation of China Grant 42372001.
文摘Dear Editor,Chibanian(Middle Pleistocene)hominin fossils that could not be easily assigned to Homo erectus,H.neanderthalensis,or H.sapiens have traditionally been as-signed to an alinclusive group:"archaic H.sapiens."In an insightful observation of the Chibanian record almost four decades ago however,Tattersall railed against the use of the word"archaic"in this sense when referring to the human fossil record,as he justifiably noted that no other biological organism has the word"archaic"attached to it'For example,no one refers to an earlier version of Canis domesticus as"archaic"C.domesticus.The ancestor of the domestic dog is,and always has been,considered to be C.lupus.In Tattersall's opinion,it would seem that these"archaic H.sapiens"fossils should be assigned to one or more formal taxonomic names.As such,terms such as"archaic H.sapiens,""mid-Pleistocene Homo,"and"Middle Pleistocene Homo"have al-ways been considered to be wastebasket taxa that include way too much morphological variabilty for one proposed taxonomic group.Continuing to use wastebasket taxa only hinders any attempts to understand true phylogenetic and evolutionary relationships.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.62106010,61976010,62176011,62236010.
文摘Image emotion classification(IEC)aims to extract the abstract emotions evoked in images.Recently,language-supervised methods such as con-trastive language-image pretraining(CLIP)have demonstrated superior performance in image under-standing.However,the underexplored task of IEC presents three major challenges:a tremendous training objective gap between pretraining and IEC,shared suboptimal prompts,and invariant prompts for all instances.In this study,we propose a general framework that effectively exploits the language-supervised CLIP method for the IEC task.First,a prompt-tuning method that mimics the pretraining objective of CLIP is introduced,to exploit the rich image and text semantics associated with CLIP.Subsequently,instance-specific prompts are automatically composed,conditioning them on the categories and image content of instances,diversifying the prompts,and thus avoiding suboptimal problems.Evaluations on six widely used affective datasets show that the proposed method significantly outperforms state-of-the-art methods(up to 9.29%accuracy gain on the EmotionROI dataset)on IEC tasks with only a few trained parameters.The code is publicly available at https://github.com/dsn0w/PT-DPC/for research purposes.
文摘ChatGPT has emerged as a promising advanced large language model that needs prompt to gain information.However,designing a good prompt is not an easy task for many end-users.Therefore,this study intends to determine the amount of information gained because of varied amounts of information in the prompt.This study used two types of prompts,initial and improved,to query the introduction sections of 327 highly cited articles on traffic safety.The queried introduction sections were then matched with the corresponding human-written introduction sections from the same articles.Similarity tests and text network analysis were used to understand the level of similarities and the content of ChatGPT-generated and human-written introductions.The findings indicate the improved prompts,which have the addition of generic persona and information about the citations and references,changed the ChatGPT's output insignificantly.While the perfect similar contents are supposed to have a 1.0 similarity score,the initial and improved prompt's introduction materials have average similarity scores of 0.5387 and 0.5567,respectively.Further,the content analysis revealed that themes such as statistics,trends,safety measures,and safety technologies are more likely to have high similarity scores,irrespective of the amount of information provided in the prompt.On the other hand,themes such as human behavior,policy and regulations,public perception,and emerging technologies require a detailed level of information in their prompt to produce materials that are close to human-written materials.The prompt engineers can use the findings to evaluate their outputs and improve their prompting skills.
文摘Artificial intelligence(AI)is advancing swiftly and integrating into various societal domains,including international Chinese language teaching.While AI provides diverse advantages,it also presents inherent risks,akin to a“double-edged sword”.This paper delves into the challenges and opportunities associated with AI and suggests a strategy to transform AI from a potential adversary to an ally:not avoidance,but mastery.Mastery entails crafting targeted prompts suitable for distinct contexts to attain desired AI-driven outcomes.Ultimately,by presenting case studies across various proficiency levels in Chinese language instruction that utilize AI tools,this paper aims to stimulate constructive dialogue within the academic community of Chinese language teaching.
基金supported by the National Natural Science Foundation of China(62222212).
文摘Information extraction(IE)aims to automatically identify and extract information about specific interests from raw texts.Despite the abundance of solutions based on fine-tuning pretrained language models,IE in the context of fewshot and zero-shot scenarios remains highly challenging due to the scarcity of training data.Large language models(LLMs),on the other hand,can generalize well to unseen tasks with few-shot demonstrations or even zero-shot instructions and have demonstrated impressive ability for a wide range of natural language understanding or generation tasks.Nevertheless,it is unclear,whether such effectiveness can be replicated in the task of IE,where the target tasks involve specialized schema and quite abstractive entity or relation concepts.In this paper,we first examine the validity of LLMs in executing IE tasks with an established prompting strategy and further propose multiple types of augmented prompting methods,including the structured fundamental prompt(SFP),the structured interactive reasoning prompt(SIRP),and the voting-enabled structured interactive reasoning prompt(VESIRP).The experimental results demonstrate that while directly promotes inferior performance,the proposed augmented prompt methods significantly improve the extraction accuracy,achieving comparable or even better performance(e.g.,zero-shot FewNERD,FewNERD-INTRA)than state-of-theart methods that require large-scale training samples.This study represents a systematic exploration of employing instruction-following LLM for the task of IE.It not only establishes a performance benchmark for this novel paradigm but,more importantly,validates a practical technical pathway through the proposed prompt enhancement method,offering a viable solution for efficient IE in low-resource settings.
文摘Large Language Models(LLMs)have significantly advanced human-computer interaction by improving natural language understanding and generation.However,their vulnerability to adversarial prompts–carefully designed inputs that manipulate model outputs–presents substantial challenges.This paper introduces a classification-based approach to detect adversarial prompts by utilizing both prompt features and prompt response features.Elevenmachine learning models were evaluated based on key metrics such as accuracy,precision,recall,and F1-score.The results show that the Convolutional Neural Network–Long Short-Term Memory(CNN-LSTM)cascade model delivers the best performance,especially when using prompt features,achieving an accuracy of over 97%in all adversarial scenarios.Furthermore,the Support Vector Machine(SVM)model performed best with prompt response features,particularly excelling in prompt type classification tasks.Classification results revealed that certain types of adversarial attacks,such as“Word Level”and“Adversarial Prefix”,were particularly difficult to detect,as indicated by their low recall and F1-scores.These findings suggest that more subtle manipulations can evade detection mechanisms.In contrast,attacks like“Sentence Level”and“Adversarial Insertion”were easier to identify,due to the model’s effectiveness in recognizing inserted content.Natural Language Processing(NLP)techniques played a critical role by enabling the extraction of semantic and syntactic features from both prompts and their corresponding responses.These insights highlight the importance of combining traditional and deep learning approaches,along with advanced NLP techniques,to build more reliable adversarial prompt detection systems for LLMs.
基金funded by the Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),Faculty of Engineering and IT,University of Technology SydneyMoreover,supported by the Researchers Supporting Project,King Saud University,Riyadh,Saudi Arabia,under Ongoing Research Funding(ORF-2025-14).
文摘The development of generative architectures has resulted in numerous novel deep-learning models that generate images using text inputs.However,humans naturally use speech for visualization prompts.Therefore,this paper proposes an architecture that integrates speech prompts as input to image-generation Generative Adversarial Networks(GANs)model,leveraging Speech-to-Text translation along with the CLIP+VQGAN model.The proposed method involves translating speech prompts into text,which is then used by the Contrastive Language-Image Pretraining(CLIP)+Vector Quantized Generative Adversarial Network(VQGAN)model to generate images.This paper outlines the steps required to implement such a model and describes in detail the methods used for evaluating the model.The GAN model successfully generates artwork from descriptions using speech and text prompts.Experimental outcomes of synthesized images demonstrate that the proposed methodology can produce beautiful abstract visuals containing elements from the input prompts.The model achieved a Frechet Inception Distance(FID)score of 28.75,showcasing its capability to produce high-quality and diverse images.The proposed model can find numerous applications in educational,artistic,and design spaces due to its ability to generate images using speech and the distinct abstract artistry of the output images.This capability is demonstrated by giving the model out-of-the-box prompts to generate never-before-seen images with plausible realistic qualities.
