Problem
Research questions and friction points this paper is trying to address.
contradiction analysis
legal contracts
large language models
human-centered review
evidence-grounded
ContraVis: Evidence-Grounded Visual Analytics for Contradiction Review in Legal Contracts
🤖 AI Summary
针对法律合同中的矛盾分析问题，提出了一种基于证据和大语言模型的可视化分析系统ContraVis，通过构建段落图来辅助人类审查员进行矛盾检测。
📝 Abstract
Legal contracts are structurally complex documents in which contradictions may emerge across distant and interconnected provisions. Although large language models (LLMs) improve legal language understanding, contradiction analysis remains a human-centered and evidence-grounded review task. We present ContraVis, a visual analytics system for human-in-the-loop contradiction analysis in legal contracts. The system models contracts as typed paragraph graphs that combine explicit contractual references with semantic relationships between paragraphs. This graph plays a dual role: it conditions LLM reasoning and serves as the interactive representation the analyst explores, keeping model context and human inspection aligned across coordinated views. In a controlled comparison, graph-conditioned reasoning recovered more injected contradictions than standalone LLM analysis as contract length grew, while surfacing additional candidates for analyst validation. A formative study with contract-domain lawyers indicated that in-context evidence comparison supported contradiction validation, and we distill design implications for evidence-grounded, LLM-assisted document review.
Innovation
Methods, ideas, or system contributions that make the work stand out.
visual analytics
contradiction review
legal contracts
typed paragraph graphs
graph-conditioned reasoning
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