NLP at UC Santa Cruz at SemEval-2024 Task 5: Legal Answer Validation using Few-Shot Multi-Choice QA

Anish Pahilajani, Samyak Rajesh Jain, Devasha Trivedi
4/4/2024
cs.CLcs.AI

No official code found. Best implementation: devashat/UCSC-NLP-SemEval-2024-Task-5 (0 stars, last commit 2025-05-21).

Abstract

This paper presents our submission to the SemEval 2024 Task 5: The Legal Argument Reasoning Task in Civil Procedure. We present two approaches to solving the task of legal answer validation, given an introduction to the case, a question and an answer candidate. Firstly, we fine-tuned pre-trained BERT-based models and found that models trained on domain knowledge perform better. Secondly, we performed few-shot prompting on GPT models and found that reformulating the answer validation task to be a multiple-choice QA task remarkably improves the performance of the model. Our best submission is a BERT-based model that achieved the 7th place out of 20.

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Codebase supporting NLP at UCSC's submission to Semeval 2024 Task 5

00Shell, PythonFeb 19, 20241 years ago

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Cite this paper

@article{pahilajani2024santa,
  title  = {NLP at UC Santa Cruz at SemEval-2024 Task 5: Legal Answer Validation using Few-Shot Multi-Choice QA},
  author = {Anish Pahilajani and Samyak Rajesh Jain and Devasha Trivedi},
  year   = {2024},
  eprint = {2404.03150},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.03150v1}
}

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