Digital White Papers

I L T A W H I T E P A P E R | K N O W L E D G E M A N A G E M E N T & M A R K E T I N G T E C H N O L O G I E S 18 R O B U S T R A G - B A S E D L E G A L Q U E S T I O N A N S W E R I N G S Y S T E M S F O R K N O W L E D G E M A N A G E M E N T The issue of content overlap — where the source text is highly homogenous and lacks specificity — can significantly impact retrieval accuracy, especially when the distinguishing factors between documents are as subtle as entity names, dates, metadata, or minor modifications in clause language. contextually nuanced answers, complicating retrieval and generation tasks. Challenges such as extracting exhaustive information, addressing multi-hop questions, aggregating information from various documents, and navigating overlapping content across documents necessitate tailored approaches for the legal sector. Moreover, systems must account for document sub-setting and cross-references while balancing accuracy and efficiency. This white paper examines these challenges, highlights the limitations of current RAG systems in legal question answering, and proposes enhancements to better address the unique demands of the legal sector. Introduction Retrieval Augmented Generation (RAG)-based systems, which combine information retrieval with Large Language Model (LLM) generation, have shown great potential in addressing complex information extraction needs across various domains. RAG systems can be advantageous in the legal domain, where vast amounts of text, including contracts, policies, and other documents, are constantly processed. However, answering legal questions presents unique challenges that make the direct application of existing RAG frameworks less effective. Legal queries often require not only the retrieval of specific facts but also a deep contextual understanding and aggregation of scattered information across document repositories. The intricacies of legal language, frequent cross-references within and across documents, and multiple versions or amendments to contracts further complicate the retrieval process. Furthermore, the nature of legal repositories, which often contain thousands of highly similar documents with only minor changes in text, poses a significant challenge for traditional retrieval models. The issue of content overlap — where the source text is highly homogenous and lacks specificity — can significantly impact retrieval accuracy, especially when the distinguishing factors between documents are as subtle as entity names, dates, metadata, or minor modifications in clause language. Additionally, the need to handle multi-hop questions, where the answer to one part of a question informs the answer to another, further complicates legal question-answering tasks. The existing design of RAG systems allows for retrieving a small set of relevant passages, but legal queries often require exhaustive information from many documents, increasing latency and cost. Legal queries frequently involve complex question types, such as aggregating information across multiple documents, which current systems are not well-equipped to handle. By addressing issues such as exhaustive information extraction, aggregate questions, multi-hop questions, and content overlap, we aim to provide insights into developing more robust RAG-based question-answering systems for legal professionals seeking precise and context-aware answers. This whitepaper describes four significant challenges in developing a robust Retrieval-Augmented Generation (RAG) based legal question-answering system and proposes potential mitigation approaches. The primary goal of this work is to shift the focus from the limitations and enhancements of RAG implementations to the unique challenges posed by the legal domain and how systems

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