Efficient Prompt Optimisation for Legal Text Classification with Proxy Prompt Evaluator
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866915542628040704 |
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| author | Lee, Hyunji Li, Kevin Chenhao Grabmair, Matthias Xu, Shanshan |
| author_facet | Lee, Hyunji Li, Kevin Chenhao Grabmair, Matthias Xu, Shanshan |
| contents | Prompt optimization aims to systematically refine prompts to enhance a language model's performance on specific tasks. Fairness detection in Terms of Service (ToS) clauses is a challenging legal NLP task that demands carefully crafted prompts to ensure reliable results. However, existing prompt optimization methods are often computationally expensive due to inefficient search strategies and costly prompt candidate scoring. In this paper, we propose a framework that combines Monte Carlo Tree Search (MCTS) with a proxy prompt evaluator to more effectively explore the prompt space while reducing evaluation costs. Experiments demonstrate that our approach achieves higher classification accuracy and efficiency than baseline methods under a constrained computation budget. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08524 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Prompt Optimisation for Legal Text Classification with Proxy Prompt Evaluator Lee, Hyunji Li, Kevin Chenhao Grabmair, Matthias Xu, Shanshan Computation and Language Prompt optimization aims to systematically refine prompts to enhance a language model's performance on specific tasks. Fairness detection in Terms of Service (ToS) clauses is a challenging legal NLP task that demands carefully crafted prompts to ensure reliable results. However, existing prompt optimization methods are often computationally expensive due to inefficient search strategies and costly prompt candidate scoring. In this paper, we propose a framework that combines Monte Carlo Tree Search (MCTS) with a proxy prompt evaluator to more effectively explore the prompt space while reducing evaluation costs. Experiments demonstrate that our approach achieves higher classification accuracy and efficiency than baseline methods under a constrained computation budget. |
| title | Efficient Prompt Optimisation for Legal Text Classification with Proxy Prompt Evaluator |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.08524 |