In the era of AI,especially large models,the importance of open source has become increasingly prominent.First,open source allows innovation to avoid starting from scratch.Through iterative innovation,it promotes tech...In the era of AI,especially large models,the importance of open source has become increasingly prominent.First,open source allows innovation to avoid starting from scratch.Through iterative innovation,it promotes technical exchanges and learning globally.Second,resources required for large model R&D are difficult for a single institution to obtain.The evaluation of general large models also requires the participation of experts from various industries.Third,without open source collaboration,it is difficult to form a unified upper-layer software ecosystem.Therefore,open source has become an important cooperation mechanism to promote the development of AI and large models.There are two cases to illustrate how open source and international standards interact with each other.展开更多
The purpose of this paper is to explore the application of large language models(LLMs)in legal case retrieval and to evaluate their potential for providing legal professionals with more efficient work aids.Currently,a...The purpose of this paper is to explore the application of large language models(LLMs)in legal case retrieval and to evaluate their potential for providing legal professionals with more efficient work aids.Currently,although pre-trained models have made great progress in legal case retrieval,they are often limited to specific types of law(e.g.,criminal law,civil law,etc.)and lack the ability to generalize across different types of law.Moreover,most models can only deal with a single task,whereas the legal case retrieval task requires a model to have a superb comprehension of legal texts,involving multiple subtasks and requiring multitasking capabilities.Therefore,the large language model,which has super generalization and multitasking ability,can solve the above problems.In order to explore the application of large language models for legal case retrieval in the legal domain,this paper evaluates a series of emerging large language models,including multilingual models,homegrown large models,and models specifically designed for the legal domain.These models are used to retrieve legal cases and its associated subtasks.Based on the Supreme People’s Court definition,the legal case retrieval task is broken down into seven subtasks:event detection,fact generation,trigger word extraction,keyword extraction,summarization,dispute focus identification,and reasoning generation.Using a variety of evaluation metrics,the experiments demonstrated that these emerging models have significant potential in the field of legal case retrieval,even with few shot samples.The research in this paper not only introduces new ideas in the field of legal case retrieval,but also empirically verifies the potential of LLMs to improve the quality and efficiency of retrieval.It proves the value of large language models in this field and is expected to significantly enhance the efficiency of legal practitioners,as well as promote the consistency and fairness of legal judgments through the use of emerging technologies.展开更多
The emergence of Medical Large Language Models has significantly transformed healthcare.Medical Large Language Models(Med-LLMs)serve as transformative tools that enhance clinical practice through applications in decis...The emergence of Medical Large Language Models has significantly transformed healthcare.Medical Large Language Models(Med-LLMs)serve as transformative tools that enhance clinical practice through applications in decision support,documentation,and diagnostics.This evaluation examines the performance of leading Med-LLMs,including GPT-4Med,Med-PaLM,MEDITRON,PubMedGPT,and MedAlpaca,across diverse medical datasets.It provides graphical comparisons of their effectiveness in distinct healthcare domains.The study introduces a domain-specific categorization system that aligns these models with optimal applications in clinical decision-making,documentation,drug discovery,research,patient interaction,and public health.The paper addresses deployment challenges of Medical-LLMs,emphasizing trustworthiness and explainability as essential requirements for healthcare AI.It presents current evaluation techniques that improve model transparency in high-stakes medical contexts and analyzes regulatory frameworks using benchmarking datasets such asMedQA,MedMCQA,PubMedQA,and MIMIC.By identifying ongoing challenges in biasmitigation,reliability,and ethical compliance,thiswork serves as a resource for selecting appropriate Med-LLMs and outlines future directions in the field.This analysis offers a roadmap for developing Med-LLMs that balance technological innovation with the trust and transparency required for clinical integration,a perspective often overlooked in existing literature.展开更多
文摘In the era of AI,especially large models,the importance of open source has become increasingly prominent.First,open source allows innovation to avoid starting from scratch.Through iterative innovation,it promotes technical exchanges and learning globally.Second,resources required for large model R&D are difficult for a single institution to obtain.The evaluation of general large models also requires the participation of experts from various industries.Third,without open source collaboration,it is difficult to form a unified upper-layer software ecosystem.Therefore,open source has become an important cooperation mechanism to promote the development of AI and large models.There are two cases to illustrate how open source and international standards interact with each other.
基金supported by the Large-scale Industry Model Evaluation Capability Development(CXFZ2024004)the National Social Science Foundation of China(22ZD035)the Research Innovation Project Plan of China University of Political Science and Law(24KYGH021).
文摘The purpose of this paper is to explore the application of large language models(LLMs)in legal case retrieval and to evaluate their potential for providing legal professionals with more efficient work aids.Currently,although pre-trained models have made great progress in legal case retrieval,they are often limited to specific types of law(e.g.,criminal law,civil law,etc.)and lack the ability to generalize across different types of law.Moreover,most models can only deal with a single task,whereas the legal case retrieval task requires a model to have a superb comprehension of legal texts,involving multiple subtasks and requiring multitasking capabilities.Therefore,the large language model,which has super generalization and multitasking ability,can solve the above problems.In order to explore the application of large language models for legal case retrieval in the legal domain,this paper evaluates a series of emerging large language models,including multilingual models,homegrown large models,and models specifically designed for the legal domain.These models are used to retrieve legal cases and its associated subtasks.Based on the Supreme People’s Court definition,the legal case retrieval task is broken down into seven subtasks:event detection,fact generation,trigger word extraction,keyword extraction,summarization,dispute focus identification,and reasoning generation.Using a variety of evaluation metrics,the experiments demonstrated that these emerging models have significant potential in the field of legal case retrieval,even with few shot samples.The research in this paper not only introduces new ideas in the field of legal case retrieval,but also empirically verifies the potential of LLMs to improve the quality and efficiency of retrieval.It proves the value of large language models in this field and is expected to significantly enhance the efficiency of legal practitioners,as well as promote the consistency and fairness of legal judgments through the use of emerging technologies.
文摘The emergence of Medical Large Language Models has significantly transformed healthcare.Medical Large Language Models(Med-LLMs)serve as transformative tools that enhance clinical practice through applications in decision support,documentation,and diagnostics.This evaluation examines the performance of leading Med-LLMs,including GPT-4Med,Med-PaLM,MEDITRON,PubMedGPT,and MedAlpaca,across diverse medical datasets.It provides graphical comparisons of their effectiveness in distinct healthcare domains.The study introduces a domain-specific categorization system that aligns these models with optimal applications in clinical decision-making,documentation,drug discovery,research,patient interaction,and public health.The paper addresses deployment challenges of Medical-LLMs,emphasizing trustworthiness and explainability as essential requirements for healthcare AI.It presents current evaluation techniques that improve model transparency in high-stakes medical contexts and analyzes regulatory frameworks using benchmarking datasets such asMedQA,MedMCQA,PubMedQA,and MIMIC.By identifying ongoing challenges in biasmitigation,reliability,and ethical compliance,thiswork serves as a resource for selecting appropriate Med-LLMs and outlines future directions in the field.This analysis offers a roadmap for developing Med-LLMs that balance technological innovation with the trust and transparency required for clinical integration,a perspective often overlooked in existing literature.
