It Cannot Be Right If It Was Written by AI: On Lawyers' Preferences of Documents Perceived as Authored by an LLM vs a Human
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Abstract (arXiv)
Large Language Models (LLMs) enable a future in which certain types of legal documents may be generated automatically. This has a great potential to streamline legal processes, lower the cost of legal services, and dramatically increase access to justice. While many researchers focus on proposing and evaluating LLM-based applications supporting tasks in the legal domain, there is a notable lack of investigations into how legal professionals perceive content if they believe an LLM has generated it. Yet, this is a critical point as over-reliance or unfounded scepticism may influence whether such documents bring about appropriate legal consequences. This study is the necessary analysis of the ongoing transition towards mature generative AI systems. Specifically, we examined whether the perception of legal documents' by lawyers and law students (n=75) varies based on their assumed origin (human-crafted vs AI-generated). The participants evaluated the documents, focusing on their correctness and language quality. Our analysis revealed a clear preference for documents perceived as crafted by a human over those believed to be generated by AI. At the same time, most participants expect the future in which documents will be generated automatically. These findings could be leveraged by legal practitioners, policymakers, and legislators to implement and adopt legal document generation technology responsibly and to fuel the necessary discussions on how legal processes should be updated to reflect recent technological developments.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 52 of its 112 references that are in the map. First posted on arXiv 9 Jul 2024.
Builds on
52 of 112 references.
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50 most cited of 52 All 52 (JSON)
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Citing papers per year: 2024: 1; 2025: 3; 2026: 1.
5 papers.
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- 2026Inventorship in AI-Assisted Inventions: Designing an Experiment to Shape Case Law15 May 2026
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- 2025CoCoLex: Confidence-guided Copy-based Decoding for Grounded Legal Text Generation7 Aug 2025
- 2025Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languages13 Feb 2025
- 2024PromptHive: Bringing Subject Matter Experts Back to the Forefront with Collaborative Prompt Engineering for Educational Content Creation21 Oct 2024
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