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 |
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creator | Bilgin, Onur Fields, Logan Laverghetta, Antonio Marji, Zaid Nighojkar, Animesh Steinle, Stephen Licato, John |
description | 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. |
doi_str_mv | 10.1007/s12626-023-00154-y |
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subjects | Business and Management Cognition & reasoning Competition policy Information Systems Applications (incl.Internet) IT in Business Language Prompt engineering Simulation and Modeling Text analysis |
title | Exploring Prompting Approaches in Legal Textual Entailment |
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