While Large Language Models (LLMs) excel in text generation and question-answering, their effectiveness in AI legal and policy is limited by outdated knowledge, hallucinations, and inadequate reasoning in complex contexts.
Retrieval-Augmented Generation (RAG) systems improve response accuracy by integrating external knowledge but struggle with retrieval errors, poor context integration, and high costs, particularly in interpreting qualitative and quantitative AI legal texts. This paper introduces a Hybrid Parameter-Adaptive RAG (HyPA-RAG) system tailored for AI legal and policy, exemplified by NYC Local Law 144 (LL144).
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