Evaluation and Improvement of Fault Detection for Large Language Models

📄 arXiv: 2404.14419v2 📥 PDF

作者: Qiang Hu, Jin Wen, Maxime Cordy, Yuheng Huang, Wei Ma, Xiaofei Xie, Lei Ma

分类: cs.SE, cs.CL, cs.LG

发布日期: 2024-04-14 (更新: 2024-11-05)

💡 一句话要点

提出MuCS框架以提升大语言模型故障检测能力

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 大语言模型 故障检测 提示变异 信心平滑 自动驾驶 医疗诊断 金融系统

📋 核心要点

  1. 现有故障检测方法在大语言模型中应用效果不明确，缺乏有效探索。
  2. 提出MuCS框架，通过提示变异技术增强现有故障检测方法的能力。
  3. 实验结果表明，MuCS框架使测试相对覆盖率提升了70.53%。

📝 摘要（中文）

大语言模型（LLMs）在多个应用领域取得了显著成功，但仍存在许多无法正确预测的故障，这影响了其可用性并可能引发安全问题。快速揭示这些故障至关重要，但数据标注过程繁重。本文首次实证研究了现有故障检测方法在LLMs上的有效性，并提出了MuCS框架，通过多种提示变异技术收集更多多样化的输出，显著提升了故障检测能力，测试相对覆盖率提高了70.53%。

🔬 方法详解

问题定义：本文旨在解决大语言模型（LLMs）在实际应用中存在的故障检测问题。现有方法在处理LLMs时效果不佳，主要由于缺乏有效的故障优先级评估和数据标注的高成本。

核心思路：论文提出的MuCS框架通过多种提示变异技术，旨在生成更多样化的模型输出，从而提高故障检测的信心平滑效果。这种设计可以有效应对LLMs在复杂任务中的不确定性。

技术框架：MuCS框架主要包括三个模块：1) 提示变异模块，通过多种变异策略生成不同的输入提示；2) 输出收集模块，收集模型对变异提示的响应；3) 信心平滑模块，利用多样化的输出进行故障检测的信心评估与提升。

关键创新：MuCS框架的核心创新在于引入提示变异技术，显著增强了现有故障检测方法的能力。这与传统方法的静态测试选择形成鲜明对比，后者通常依赖于固定的输入提示。

关键设计：在MuCS框架中，提示变异的策略包括同义词替换、句子重构等，确保生成的输出具有多样性。此外，信心平滑的计算方法采用了加权平均策略，以提高故障检测的准确性。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，MuCS框架在四个不同任务上显著提升了故障检测能力，测试相对覆盖率提高了70.53%。与传统方法相比，简单的Margin方法在LLMs上表现良好，但MuCS框架的改进效果更为显著，展示了其在实际应用中的潜力。

🎯 应用场景

该研究的潜在应用领域包括自动驾驶、医疗诊断和金融系统等对可靠性要求高的场景。通过提升大语言模型的故障检测能力，可以增强这些系统的安全性和可靠性，减少潜在的安全隐患，具有重要的实际价值和社会影响。

📄 摘要（原文）

Large language models (LLMs) have recently achieved significant success across various application domains, garnering substantial attention from different communities. Unfortunately, even for the best LLM, many \textit{faults} still exist that LLM cannot properly predict. Such faults will harm the usability of LLMs in general and could introduce safety issues in reliability-critical systems such as autonomous driving systems. How to quickly reveal these faults in real-world datasets that LLM could face is important, but challenging. The major reason is that the ground truth is necessary but the data labeling process is heavy considering the time and human effort. To handle this problem, in the conventional deep learning testing field, test selection methods have been proposed for efficiently evaluating deep learning models by prioritizing faults. However, despite their importance, the usefulness of these methods on LLMs is unclear, and lack of exploration. In this paper, we conduct the first empirical study to investigate the effectiveness of existing fault detection methods for LLMs. Experimental results on four different tasks~(including both code tasks and natural language processing tasks) and four LLMs~(e.g., LLaMA3 and GPT4) demonstrated that simple methods such as Margin perform well on LLMs but there is still a big room for improvement. Based on the study, we further propose \textbf{MuCS}, a prompt \textbf{Mu}tation-based prediction \textbf{C}onfidence \textbf{S}moothing framework to boost the fault detection capability of existing methods. Concretely, multiple prompt mutation techniques have been proposed to help collect more diverse outputs for confidence smoothing. The results show that our proposed framework significantly enhances existing methods with the improvement of test relative coverage by up to 70.53\%.