
Integrating Case-Based Reasoning (CBR) with Retrieval-Augmented Generation (RAG) in Large Language Models (LLMs) can improve the quality and factual correctness of generated answers for legal questions by providing relevant contextual information from a case-base.


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Enhancing Legal Question Answering with Case-Based Reasoning and Retrieval-Augmented Generation


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Legal AI models can improve charge prediction accuracy by leveraging domain knowledge through the innovative From Graph to Word Bag (FWGB) approach.


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From Graph to Word Bag: Enhancing Charge Prediction with Domain Knowledge



Introducing domain knowledge through a novel approach improves charge prediction in legal AI.


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From Graph to Word Bag: Enhancing Legal AI Charge Prediction with Domain Knowledge



The author introduces the FWGB approach, leveraging domain knowledge to guide models in distinguishing confusing charges effectively.


from-graph-to-word-bag-enhancing-legal-charge-prediction-with-domain-knowledge


From Graph to Word Bag: Enhancing Legal Charge Prediction with Domain Knowledge
