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Exploring Prompting Approaches in Legal Textual Entailment

Author

Listed:
  • Onur Bilgin

    (University of South Florida)

  • Logan Fields

    (University of South Florida)

  • Antonio Laverghetta

    (University of South Florida)

  • Zaid Marji

    (University of South Florida)

  • Animesh Nighojkar

    (University of South Florida)

  • Stephen Steinle

    (University of South Florida)

  • John Licato

    (University of South Florida)

Abstract

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.

Suggested Citation

  • Onur Bilgin & Logan Fields & Antonio Laverghetta & Zaid Marji & Animesh Nighojkar & Stephen Steinle & John Licato, 2024. "Exploring Prompting Approaches in Legal Textual Entailment," The Review of Socionetwork Strategies, Springer, vol. 18(1), pages 75-100, April.
    • Handle: RePEc:spr:trosos:v:18:y:2024:i:1:d:10.1007_s12626-023-00154-y
    DOI: 10.1007/s12626-023-00154-y
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    References listed on IDEAS

    as
    1. John Zhuang Liu & Xueyao Li, 2019. "Legal Techniques for Rationalizing Biased Judicial Decisions: Evidence from Experiments with Real Judges," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 16(3), pages 630-670, September.
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    Keywords

    AI; NLP; Reasoning; Law; Legal;
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