用于大语言模型中偏见控制的无监督概念向量提取
Unsupervised Concept Vector Extraction for Bias Control in LLMs
AI总结：
本文提出一种无监督概念向量提取方法，通过概率加权和投影控制来测量并操控大语言模型中的性别偏见，并验证其可泛化至种族偏见。
摘要：
大语言模型（LLMs）已知会延续刻板印象并表现出偏见。已有多种策略被提出以缓解这些偏见，但大多数工作将偏见作为黑箱问题来研究，而未考虑概念在模型内部是如何被表示的。我们借鉴表示工程（representation engineering）的技术，研究“性别”这一概念在LLMs内部是如何被表示的。我们提出了一种新方法，通过概率加权在无标注数据的情况下提取概念表示，并高效地选择一个控制向量（steering vector）来测量和操控模型的表示。我们开发了一种基于投影的方法，能够对模型预测进行精确控制，并证明其在缓解LLMs中性别偏见方面的有效性，同时表明该方法也能泛化到种族偏见。我们的代码可在以下地址获取：https://github.com/hannahxchen/gender-bias-steering
英文摘要：
Large language models (LLMs) are known to perpetuate stereotypes and exhibit biases. Various strategies have been proposed to mitigate these biases, but most work studies biases as a black-box problem without considering how concepts are represented within the model. We adapt techniques from representation engineering to study how the concept of "gender" is represented within LLMs. We introduce a new method that extracts concept representations via probability weighting without labeled data and efficiently selects a steering vector for measuring and manipulating the model's representation. We develop a projection-based method that enables precise steering of model predictions and demonstrate its effectiveness in mitigating gender bias in LLMs and show that it also generalizes to racial bias. Our code is available at: https://github.com/hannahxchen/gender-bias-steering
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。
