ContraVis: Evidence-Grounded Visual Analytics for Contradiction Review in Legal Contracts

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.

Human-Computer Interaction (cs.HC); Computation and Language (cs.CL)

arXiv:2609.27014 [cs.HC]

(or arXiv:2609.27014v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2609.27014 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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