mlsanityData to 7 Oct 2026

Contractual Deepfakes: Can Large Language Models Generate Contracts?

arXiv 2602.09384 ↗First posted 10 Feb 2026cs.CL cs.AIcited by 0

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Abstract (arXiv)

Notwithstanding their unprecedented ability to generate text, LLMs do not understand the meaning of words, have no sense of context and cannot reason. Their output constitutes an approximation of statistically dominant word patterns. And yet, the drafting of contracts is often presented as a typical legal task that could be facilitated by this technology. This paper seeks to put an end to such unreasonable ideas. Predicting words differs from using language in the circumstances of specific transactions and reconstituting common contractual phrases differs from reasoning about the law. LLMs seem to be able to generate generic and superficially plausible contractual documents. In the cold light of day, such documents may turn out to be useless assemblages of inconsistent provisions or contracts that are enforceable but unsuitable for a given transaction. This paper casts a shadow on the simplistic assumption that LLMs threaten the continued viability of the legal industry.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 16 references that are in the map. First posted on arXiv 10 Feb 2026.

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References · 6

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2024Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Toolscited by 170
  3. 2024LLM In-Context Recall is Prompt Dependentcited by 15
  4. 2024From Data to Commonsense Reasoning: The Use of Large Language Models for Explainable AIcited by 7
  5. 2025Do LLMs Truly Understand When a Precedent Is Overruled?cited by 6
  6. 2025LLMs Can Get "Brain Rot": A Pilot Study on Twitter/Xcited by 2

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  3. 2025Better Call CLAUSE: A Discrepancy Benchmark for Auditing LLMs Legal Reasoning Capabilities
  4. 2026A Few Good Clauses: Comparing LLMs vs Domain-Trained Small Language Models on Structured Contract Extraction
  5. 2024It Cannot Be Right If It Was Written by AI: On Lawyers' Preferences of Documents Perceived as Authored by an LLM vs a Human
  6. 2026LLM Agents in Law: Taxonomy, Applications, and Challenges
  7. 2026Evaluation of Large Language Models in Legal Applications: Challenges, Methods, and Future Directions
  8. 2025Structured Definitions and Segmentations for Legal Reasoning in LLMs: A Study on Indian Legal Data