CaseGPT: a case reasoning framework based on language models and retrieval-augmented generation
This paper presents CaseGPT, an innovative approach that combines Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) technology to enhance case-based reasoning in the healthcare and legal sectors. The system addresses the challenges of traditional database queries by enabling fuzzy searches based on imprecise descriptions, thereby improving data searchability and usability. CaseGPT not only retrieves relevant case data but also generates insightful suggestions and recommendations based on patterns discerned from existing case data. This functionality proves especially valuable for tasks such as medical diagnostics, legal precedent research, and case strategy formulation. The paper includes an in-depth discussion of the system's methodology, its performance in both medical and legal domains, and its potential for future applications. Our experiments demonstrate that CaseGPT significantly outperforms traditional keyword-based and simple LLM-based systems in terms of precision, recall, and efficiency.
Code (0)
등록된 구현이 없습니다.
Tasks
RAGRetrievalRetrieval-augmented GenerationSimilar Papers 제목 키워드 기반
MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval
Engineering rulebooks and technical standards contain multimodal information like dense text, tables, and illustrations that are challenging for retrieval augmented generation (RAG) systems. Building upon the DesignQA fr…
Question AnsweringV-Retrver: Evidence-Driven Agentic Reasoning for Universal Multimodal Retrieval
Multimodal Large Language Models (MLLMs) have recently been applied to universal multimodal retrieval, where Chain-of-Thought (CoT) reasoning improves candidate reranking. However, existing approaches remain largely lang…
Reinforcement LearningA General Retrieval-Augmented Generation Framework for Multimodal Case-Based Reasoning Applications
Case-based reasoning (CBR) is an experience-based approach to problem solving, where a repository of solved cases is adapted to solve new cases. Recent research shows that Large Language Models (LLMs) with Retrieval-Augm…
MathRAGRetrievalRetrieval-augmented GenerationCase-based Reasoning Augmented Large Language Model Framework for Decision Making in Realistic Safety-Critical Driving Scenarios
Driving in safety-critical scenarios requires quick, context-aware decision-making grounded in both situational understanding and experiential reasoning. Large Language Models (LLMs), with their powerful general-purpose …
Autonomous DrivingDecision MakingDomain AdaptationIn-Context Learning+5PathoScribe: Transforming Pathology Data into a Living Library with a Unified LLM-Driven Framework for Semantic Retrieval and Clinical Integration
Pathology underpins modern diagnosis and cancer care, yet its most valuable asset, the accumulated experience encoded in millions of narrative reports, remains largely inaccessible. Although institutions are rapidly digi…
Semantic RetrievalQuestion Answering