Exploring Prompting Approaches in Legal Textual Entailment

被引：0
|
作者：
Onur Bilgin
Logan Fields
Antonio Laverghetta
Zaid Marji
Animesh Nighojkar
Stephen Steinle
John Licato
机构：
[1] University of South Florida,Advancing Machine and Human Reasoning Lab, Department of Computer Science and Engineering
来源：
The Review of Socionetwork Strategies | 2024年 / 18卷
关键词：
AI; NLP; Reasoning; Law; Legal;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
We report explorations into prompt engineering with large pre-trained language models that were not fine-tuned to solve the legal entailment task (Task 4) of the 2023 COLIEE competition. Our most successful strategy used simple text similarity measures to retrieve articles and queries from the training set. We report on our efforts to optimize performance with both OpenAI’s GPT-4 and FLaN-T5. We also used an ensemble approach to find the best combination of models and prompts. Finally, we analyze our results and suggest ideas for future improvements.
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页码：75 / 100
页数：25
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