[2309.05501] Black-Box Analysis: GPTs Across Time in Legal Textual Entailment Task
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Computer Science > Computation and Language

arXiv:2309.05501 (cs)
[Submitted on 11 Sep 2023]

Title:Black-Box Analysis: GPTs Across Time in Legal Textual Entailment Task

Authors:Ha-Thanh Nguyen, Randy Goebel, Francesca Toni, Kostas Stathis, Ken Satoh
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Abstract:The evolution of Generative Pre-trained Transformer (GPT) models has led to significant advancements in various natural language processing applications, particularly in legal textual entailment. We present an analysis of GPT-3.5 (ChatGPT) and GPT-4 performances on COLIEE Task 4 dataset, a prominent benchmark in this domain. The study encompasses data from Heisei 18 (2006) to Reiwa 3 (2021), exploring the models' abilities to discern entailment relationships within Japanese statute law across different periods. Our preliminary experimental results unveil intriguing insights into the models' strengths and weaknesses in handling legal textual entailment tasks, as well as the patterns observed in model performance. In the context of proprietary models with undisclosed architectures and weights, black-box analysis becomes crucial for evaluating their capabilities. We discuss the influence of training data distribution and the implications on the models' generalizability. This analysis serves as a foundation for future research, aiming to optimize GPT-based models and enable their successful adoption in legal information extraction and entailment applications.
Comments: ISAILD@KSE 2023
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2309.05501 [cs.CL]
  (or arXiv:2309.05501v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2309.05501

Submission history

From: Ha Thanh Nguyen [view email]
[v1] Mon, 11 Sep 2023 14:43:54 UTC (1,093 KB)
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