Streamlining Industrial Contract Management with Retrieval-Augmented LLMs

TL;DR

This paper introduces a retrieval-augmented generation framework to automate and improve the efficiency of industrial contract management, especially in low-resource settings with unstructured legacy contracts.

Contribution

It presents a modular RAG-based system combining synthetic data, clause retrieval, acceptability classification, and reward alignment for contract revision automation.

Findings

Achieves over 80% accuracy in identifying problematic revisions

Effective in low-resource, real-world contract scenarios

Speeds up contract revision workflows

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.