Exploring Prompting Approaches in Legal Textual Entailment

Exploring Prompting Approaches in Legal Textual Entailment

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 train...

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Veröffentlicht in:The review of socionetwork strategies 2024, Vol.18 (1), p.75-100
Hauptverfasser: Bilgin, Onur, Fields, Logan, Laverghetta, Antonio, Marji, Zaid, Nighojkar, Animesh, Steinle, Stephen, Licato, John
Format: Artikel
Sprache:eng
Schlagworte:
Business and Management
Cognition & reasoning
Competition policy
Information Systems Applications (incl.Internet)
IT in Business
Language
Prompt engineering
Simulation and Modeling
Text analysis
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Beschreibung
Zusammenfassung: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.
ISSN:2523-3173
1867-3236
DOI:10.1007/s12626-023-00154-y