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LexDrafter: Terminology Drafting for Legislative Documents using Retrieval Augmented Generation
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With the increase in legislative documents at the EU, the number of new terms and their definitions is increasing as well. As per the Joint Practical Guide of the European Parliament, the Council and the Commission, terms used in legal documents shall be consistent, and identical concepts shall be expressed without departing from their meaning in ordinary, legal, or technical language. Thus, while drafting a new legislative document, having a framework that provides insights about existing definitions and helps define new terms based on a document's context will support such harmonized legal definitions across different regulations and thus avoid ambiguities. In this paper, we present LexDrafter, a framework that assists in drafting Definitions articles for legislative documents using retrieval augmented generation (RAG) and existing term definitions present in different legislative documents. For this, definition elements are built by extracting definitions from existing documents. Using definition elements and RAG, a Definitions article can be suggested on demand for a legislative document that is being drafted. We demonstrate and evaluate the functionality of LexDrafter using a collection of EU documents from the energy domain. The code for LexDrafter framework is available at https://github.com/achouhan93/LexDrafter.
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Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal Reasoning
LSIM combines reinforcement-learned fact-rule chains, a trainable DSSM retriever, and in-context learning to improve legal QA output over semantic-only RAG baselines by about 2 to 3 points on METEOR and ROUGE-1.
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