@inproceedings{zhao-etal-2024-hw,
title = "{HW}-{TSC} at {S}em{E}val-2024 Task 5: Self-Eval? A Confident {LLM} System for Auto Prediction and Evaluation for the Legal Argument Reasoning Task",
author = "Zhao, Xiaofeng and
Qiao, Xiaosong and
Ou, Kaiwen and
Zhang, Min and
Chang, Su and
Piao, Mengyao and
Li, Yuang and
Li, Yinglu and
Zhu, Ming and
Liu, Yilun",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.255",
doi = "10.18653/v1/2024.semeval-1.255",
pages = "1806--1810",
abstract = "In this article, we present an effective system for semeval-2024 task 5. The task involves assessing the feasibility of a given solution in civil litigation cases based on relevant legal provisions, issues, solutions, and analysis. This task demands a high level of proficiency in U.S. law and natural language reasoning. In this task, we designed a self-eval LLM system that simultaneously performs reasoning and self-assessment tasks. We created a confidence interval and a prompt instructing the LLM to output the answer to a question along with its confidence level. We designed a series of experiments to prove the effectiveness of the self-eval mechanism. In order to avoid the randomness of the results, the final result is obtained by voting on three results generated by the GPT-4. Our submission was conducted under zero-resource setting, and we achieved first place in the task with an F1-score of 0.8231 and an accuracy of 0.8673.",
}
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<abstract>In this article, we present an effective system for semeval-2024 task 5. The task involves assessing the feasibility of a given solution in civil litigation cases based on relevant legal provisions, issues, solutions, and analysis. This task demands a high level of proficiency in U.S. law and natural language reasoning. In this task, we designed a self-eval LLM system that simultaneously performs reasoning and self-assessment tasks. We created a confidence interval and a prompt instructing the LLM to output the answer to a question along with its confidence level. We designed a series of experiments to prove the effectiveness of the self-eval mechanism. In order to avoid the randomness of the results, the final result is obtained by voting on three results generated by the GPT-4. Our submission was conducted under zero-resource setting, and we achieved first place in the task with an F1-score of 0.8231 and an accuracy of 0.8673.</abstract>
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%0 Conference Proceedings
%T HW-TSC at SemEval-2024 Task 5: Self-Eval? A Confident LLM System for Auto Prediction and Evaluation for the Legal Argument Reasoning Task
%A Zhao, Xiaofeng
%A Qiao, Xiaosong
%A Ou, Kaiwen
%A Zhang, Min
%A Chang, Su
%A Piao, Mengyao
%A Li, Yuang
%A Li, Yinglu
%A Zhu, Ming
%A Liu, Yilun
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F zhao-etal-2024-hw
%X In this article, we present an effective system for semeval-2024 task 5. The task involves assessing the feasibility of a given solution in civil litigation cases based on relevant legal provisions, issues, solutions, and analysis. This task demands a high level of proficiency in U.S. law and natural language reasoning. In this task, we designed a self-eval LLM system that simultaneously performs reasoning and self-assessment tasks. We created a confidence interval and a prompt instructing the LLM to output the answer to a question along with its confidence level. We designed a series of experiments to prove the effectiveness of the self-eval mechanism. In order to avoid the randomness of the results, the final result is obtained by voting on three results generated by the GPT-4. Our submission was conducted under zero-resource setting, and we achieved first place in the task with an F1-score of 0.8231 and an accuracy of 0.8673.
%R 10.18653/v1/2024.semeval-1.255
%U https://aclanthology.org/2024.semeval-1.255
%U https://doi.org/10.18653/v1/2024.semeval-1.255
%P 1806-1810
Markdown (Informal)
[HW-TSC at SemEval-2024 Task 5: Self-Eval? A Confident LLM System for Auto Prediction and Evaluation for the Legal Argument Reasoning Task](https://aclanthology.org/2024.semeval-1.255) (Zhao et al., SemEval 2024)
ACL
- Xiaofeng Zhao, Xiaosong Qiao, Kaiwen Ou, Min Zhang, Su Chang, Mengyao Piao, Yuang Li, Yinglu Li, Ming Zhu, and Yilun Liu. 2024. HW-TSC at SemEval-2024 Task 5: Self-Eval? A Confident LLM System for Auto Prediction and Evaluation for the Legal Argument Reasoning Task. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1806–1810, Mexico City, Mexico. Association for Computational Linguistics.