用代理评估器加速法律条款公平性检测的提示优化
Efficient Prompt Optimisation for Legal Text Classification with Proxy Prompt Evaluator
- 结合蒙特卡洛树搜索与代理评估器，高效探索提示空间
- 在有限计算资源下，准确率和效率均优于基线方法
- 适合需要低成本高精度法律文本分类的研究者
提示优化旨在系统性地改进提示以提升语言模型在特定任务上的表现。服务条款中公平性检测是一项具有挑战性的法律自然语言处理任务，需要精心设计的提示以确保结果可靠。然而，现有提示优化方法通常因低效的搜索策略和高昂的提示候选评分成本而计算开销大。本文提出一种框架，将蒙特卡洛树搜索（MCTS）与代理提示评估器结合，更有效地探索提示空间，同时降低评估成本。实验表明，在受限计算预算下，该方法在分类准确率和效率上均优于基线方法。
原文摘要 · Abstract (English)
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.
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