解码偏见：用于语言模型中性别偏见检测的自动化方法与LLM评判器
Decoding Biases: Automated Methods and LLM Judges for Gender Bias Detection in Language Models
AI总结：
针对LLM性别偏见评估缺乏标准且依赖人工的问题，本文训练模型自动生成对抗性提示以诱发偏见回复，并提出基于LLM的评估指标，验证LLM-as-a-Judge与人类判断一致。
摘要：
大语言模型（LLM）在语言理解和生成人类水平文本方面表现出色。然而，即使经过监督训练和人类对齐，这些LLM仍容易受到对抗攻击，恶意用户可通过提示诱导模型生成不良文本。LLM还固有地编码了潜在偏见，这些偏见会在交互过程中造成各种有害影响。偏见评估指标缺乏标准与共识，现有方法通常依赖人工生成的模板和标注，成本高昂且耗费人力。在本研究中，我们训练模型自动创建对抗性提示，以诱发目标LLM产生带有偏见的回复。我们提出了基于LLM的偏见评估指标，并分析了若干现有的自动评估方法和指标。我们分析了模型回复中的各种细微差别，识别了不同模型系列的优势与弱点，并评估了评估方法在哪些方面存在不足。我们将这些指标与人工评估进行比较，验证了LLM-as-a-Judge指标在回复生成中的偏见判断上与人类判断相一致。
英文摘要：
Large Language Models (LLMs) have excelled at language understanding and generating human-level text. However, even with supervised training and human alignment, these LLMs are susceptible to adversarial attacks where malicious users can prompt the model to generate undesirable text. LLMs also inherently encode potential biases that can cause various harmful effects during interactions. Bias evaluation metrics lack standards as well as consensus and existing methods often rely on human-generated templates and annotations which are expensive and labor intensive. In this work, we train models to automatically create adversarial prompts to elicit biased responses from target LLMs. We present LLM- based bias evaluation metrics and also analyze several existing automatic evaluation methods and metrics. We analyze the various nuances of model responses, identify the strengths and weaknesses of model families, and assess where evaluation methods fall short. We compare these metrics to human evaluation and validate that the LLM-as-a-Judge metric aligns with human judgement on bias in response generation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
