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LLMs can reconstruct missing contract terms with high accuracy, study finds

By PulseAugur Editorial · [1 sources] ·

A new paper titled "Generative Gap Filling" explores the ability of large language models to reconstruct missing terms in legal contracts. Researchers found that LLMs could predict the masked contract terms with nearly 90% accuracy, significantly outperforming human participants. This suggests that contracts contain more implicit information about the parties' agreements than previously understood, potentially offering courts a new form of evidence. AI

IMPACT This research suggests LLMs could revolutionize legal contract analysis and dispute resolution by providing more accurate gap-filling capabilities.

RANK_REASON The cluster contains a research paper detailing a novel application of LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs can reconstruct missing contract terms with high accuracy, study finds

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The cluster contains a research paper detailing a novel application of LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yonathan A. Arbel, David A. Hoffman ·

    Generative Gap Filling

    arXiv:2608.21401v1 Announce Type: cross Abstract: Most contract litigation turns on contracts that imperfectly record parties' bargains. When the parties' dispute can't be solved by interpreting the text, courts fill the gap. Scholars have long assumed that the remaining text run…

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