Contractual Deepfakes: Can Large Language Models Generate Contracts?
- URL: http://arxiv.org/abs/2602.09384v1
- Date: Tue, 10 Feb 2026 03:52:29 GMT
- Title: Contractual Deepfakes: Can Large Language Models Generate Contracts?
- Abstract summary: LLMs do not understand the meaning of words, have no sense of context and cannot reason.<n>Their output constitutes an approximation of statistically dominant word patterns.<n>This paper seeks to put an end to such unreasonable ideas.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
Related papers
- Towards Robust Legal Reasoning: Harnessing Logical LLMs in Law [0.0]
Large language models (LLMs) show promise, but their application in legal contexts demands higher accuracy, repeatability, and transparency.<n>We propose a neuro-symbolic approach that integrates LLMs' natural language understanding with logic-based reasoning.<n>As a legal document case study, we applied neuro-symbolic AI to coverage-related queries in insurance contracts.
arXiv Detail & Related papers (2025-02-24T20:38:17Z) - Ambiguity is the last thing you need [0.0]
Clear legal language forms the backbone of a contract for numerous reasons.
Disputes often arise between contract parties where ambiguous language has been used.
Unambiguous language can also be important where there is an imbalance of bargaining strength between the parties.
arXiv Detail & Related papers (2024-10-26T16:35:45Z) - Caveat Lector: Large Language Models in Legal Practice [0.0]
The fascination with large language models derives from the fact that many users lack the expertise to evaluate the quality of the generated text.
The dangerous combination of fluency and superficial plausibility leads to the temptation to trust the generated text and creates the risk of overreliance.
arXiv Detail & Related papers (2024-03-14T08:19:41Z) - Silent Guardian: Protecting Text from Malicious Exploitation by Large Language Models [63.91178922306669]
We introduce Silent Guardian, a text protection mechanism against large language models (LLMs)
By carefully modifying the text to be protected, TPE can induce LLMs to first sample the end token, thus directly terminating the interaction.
We show that SG can effectively protect the target text under various configurations and achieve almost 100% protection success rate in some cases.
arXiv Detail & Related papers (2023-12-15T10:30:36Z) - BLT: Can Large Language Models Handle Basic Legal Text? [44.89873147675516]
GPT-4 and Claude perform poorly on basic legal text handling.
Poor performance on benchmark casts into doubt their reliability as-is for legal practice.
Fine-tuning on training set brings even a small model to near-perfect performance.
arXiv Detail & Related papers (2023-11-16T09:09:22Z) - A Robust Semantics-based Watermark for Large Language Model against Paraphrasing [50.84892876636013]
Large language models (LLMs) have show great ability in various natural language tasks.
There are concerns that LLMs are possible to be used improperly or even illegally.
We propose a semantics-based watermark framework SemaMark.
arXiv Detail & Related papers (2023-11-15T06:19:02Z) - Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model
Collaboration [52.57055162778548]
Legal Judgment Prediction (LJP) has become an increasingly crucial task in Legal AI.
Precedents are the previous legal cases with similar facts, which are the basis for the judgment of the subsequent case in national legal systems.
Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task.
arXiv Detail & Related papers (2023-10-13T16:47:20Z) - Generative Interpretation [0.0]
We introduce generative interpretation, a new approach to estimating contractual meaning using large language models.
We show that AI models can help factfinders ascertain ordinary meaning in context, quantify ambiguity, and fill gaps in parties' agreements.
arXiv Detail & Related papers (2023-08-14T02:59:27Z) - Are Representations Built from the Ground Up? An Empirical Examination
of Local Composition in Language Models [91.3755431537592]
Representing compositional and non-compositional phrases is critical for language understanding.
We first formulate a problem of predicting the LM-internal representations of longer phrases given those of their constituents.
While we would expect the predictive accuracy to correlate with human judgments of semantic compositionality, we find this is largely not the case.
arXiv Detail & Related papers (2022-10-07T14:21:30Z) - Detecting Logical Relation In Contract Clauses [94.85352502638081]
We develop an approach to automate the extraction of logical relations between clauses in a contract.
The resulting approach should help contract authors detecting potential logical conflicts between clauses.
arXiv Detail & Related papers (2021-11-02T19:26:32Z) - ContractNLI: A Dataset for Document-level Natural Language Inference for
Contracts [39.75232199445175]
We propose "document-level natural language inference (NLI) for contracts"
A system is given a set of hypotheses and a contract, and it is asked to classify whether each hypothesis is "entailed by", "contradicting to" or "not mentioned by" (neutral to) the contract.
We release the largest corpus to date consisting of 607 annotated contracts.
arXiv Detail & Related papers (2021-10-05T03:22:31Z) - Lexically-constrained Text Generation through Commonsense Knowledge
Extraction and Injection [62.071938098215085]
We focus on the Commongen benchmark, wherein the aim is to generate a plausible sentence for a given set of input concepts.
We propose strategies for enhancing the semantic correctness of the generated text.
arXiv Detail & Related papers (2020-12-19T23:23:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.