2024 UtilizingLargeLanguageModelstoA

• (Zhao, Yang, Gao, 2024, 2024) ⇒ Yu Zhao, Shiqi Yang, and Haoxiang Gao. (2024). “Utilizing Large Language Models to Analyze Common Law Contract Formation.” OSF Preprints.

Subject Headings: Contract Formation, Contract Classification.

Notes

• The primary NLP task in the paper is the binary classification of the presence or absence of each contract formation element (offer, acceptance, consideration, and defenses) within fictional situations used for educational purposes to demonstrate the principles of contract formation
• It explores the application of Large Language Models (LLMs) in analyzing the elements of contract formation, which is crucial in common law jurisdictions like the United States and the United Kingdom.
• It breaks down contract formation into four essential elements: offer, acceptance, consideration, and defenses, assessing each element's presence in hypothetical cases to determine contract viability.
• It proposes a multi-task learning model designed to identify the presence of each contract element and gauge the confidence level of these predictions.
• It leverages the capabilities of GPT-4, a transformer-based LLM, to interpret complex legal texts and assess contract formation elements based on pre-trained and fine-tuned models.
• It emphasizes the importance of supervised fine-tuning of LLMs on labeled datasets tailored to contract law formation, enhancing the model's ability to respond accurately to legal scenarios.
• Its use of multi-task learning allows the model to assess multiple elements of contract formation simultaneously, with each task head producing a binary classification output.
• It uses model evaluation metrics like binary cross-entropy loss, accuracy, precision, and recall to measure the effectiveness of the multi-task model in detecting contract formation elements.
• It highlights the potential of LLMs to streamline legal research by quickly sifting through extensive legal databases to find relevant case law, statutes, and legal literature.
• It discusses the application of LLMs in regulatory compliance, where they can monitor and interpret regulatory changes continuously, ensuring compliance with evolving legal standards.
• Its future applications of LLMs in contract analysis include automating contract drafting, enhancing contract compliance monitoring, and assisting in dispute resolution by analyzing contractual disputes and legal arguments.
• It concludes that despite challenges, LLMs offer valuable tools for analyzing textual inputs and assessing the presence of essential contract formation elements, with ongoing advancements expected to refine these capabilities further.

Cited By

• http://scholar.google.com/scholar?q=%222024%22+Utilizing+Large+Language+Models+to+Analyze+Common+Law+Contract+Formation

Quotes

Abstract

This article explores the application of Large Language Models (LLMs) and multi-task learning in evaluating the validity of contract formation scenarios. The article breaks down contract formation into four essential elements: offer, acceptance, consideration, and defenses, and then assesses the presence of each element within a hypothetical case to determine the viability of contract formation.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2024 UtilizingLargeLanguageModelstoA	Yu Zhao Haoxiang Gao Shiqi Yang			Utilizing Large Language Models to Analyze Common Law Contract Formation						2024