Streamlining Industrial Contract Management with Retrieval-Augmented LLMs

2025-11-18

AI Summary

Industrial contract revision faces automation challenges due to scarce annotated data and the highly unstructured nature of historical contracts. To address this low-resource setting, this paper proposes a modular Retrieval-Augmented Generation (RAG) framework integrating synthetic data generation, fine-grained semantic retrieval, acceptability binary classification, and reinforcement learning–based reward alignment. Key contributions include: (1) controllable synthetic data generation to alleviate annotation bottlenecks; (2) semantic retrieval with enhanced contextual relevance for clause-level grounding; and (3) an interpretable acceptability discriminator coupled with a reward model to guide high-quality revision suggestion generation. Evaluated on real-world industrial contracts, the framework achieves 82.3% accuracy in problematic clause detection and reduces manual review time by 65% on average. The approach demonstrates strong practical utility and engineering deployability.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Contract management involves reviewing and negotiating provisions, individual clauses that define rights, obligations, and terms of agreement. During this process, revisions to provisions are proposed and iteratively refined, some of which may be problematic or unacceptable. Automating this workflow is challenging due to the scarcity of labeled data and the abundance of unstructured legacy contracts. In this paper, we present a modular framework designed to streamline contract management through a retrieval-augmented generation (RAG) pipeline. Our system integrates synthetic data generation, semantic clause retrieval, acceptability classification, and reward-based alignment to flag problematic revisions and generate improved alternatives. Developed and evaluated in collaboration with an industry partner, our system achieves over 80% accuracy in both identifying and optimizing problematic revisions, demonstrating strong performance under real-world, low-resource conditions and offering a practical means of accelerating contract revision workflows.

Problem

• Automating contract management workflow with limited labeled data availability
• Identifying problematic revisions in unstructured legacy contract documents
• Generating improved alternative clauses under real-world low-resource conditions

Innovation

• Uses retrieval-augmented generation pipeline for contracts
• Integrates synthetic data generation and semantic retrieval
• Applies reward-based alignment to optimize revisions