通过神经元干预实现语言模型性别生成的精准控制。
Neuron-Level Interventions for Gendered and Gender-Neutral Generation in Language Models
- 定位并操控与性别相关的特定神经元，实现对生成内容的性别导向控制。
- 实验显示早期层神经元集中编码性别信息，且干预后保持语义一致性。
- 适用于性别偏见缓解与可控生成研究，适合关注公平性与可控性的开发者。
语言模型在中性提示下仍可能生成带有性别刻板印象的内容。现有研究多聚焦于男女二元对立，忽视了they/them等中性表达。本文研究了语言模型中与女性、男性及中性三类性别相关的神经元，提出一种基于神经元层级的干预方法，通过激活或屏蔽特定神经元，可有效引导生成目标性别形式，同时保持原意不变。我们构建了两个涵盖三类性别的控制句数据集，并通过人工评估验证质量。在两个开源模型上的实验表明，性别相关神经元主要集中在早期层，后期贡献较小。相比现有方法，本方法实现更精准的性别控制，非目标性别泄漏更低，输出质量更稳定。本工作揭示了性别在模型中的编码机制，为神经元干预评估与偏见缓解提供了有效工具。代码与数据集已公开。
原文摘要 · Abstract (English)
Language models (LMs) can produce gendered language and stereotypes even when given neutral prompts. Most prior work on gender bias in LMs primarily examines gender through a binary lens (feminine vs. masculine), with limited attention to gender-neutral forms, such as they/them pronouns or neutrally phrased job titles. How gender-related signals are encoded in the internal representations of LMs remains an open question. In this work, we study gender-specific neurons in LMs across three categories: feminine, masculine, and gender-neutral. We propose a neuron-level intervention method to identify neurons that are strongly tied to each gender category. We then test these neurons through controlled generation, showing that activating or masking gender-related neurons can steer a sentence toward a target gender form while preserving its original meaning. To evaluate the effectiveness of our gender-intervention approach, we curate two datasets with controlled sentences labeled across all three gender categories and validate the data quality through human evaluation. Experiments on two open-source LMs show that gender-specific neurons are not evenly distributed across model layers; instead, they concentrate heavily in the earliest layers with smaller contributions from later layers. Compared to existing methods, our method achieves more precise gender control, with less leakage into non-target gender categories and stable output quality through two evaluation criteria. Overall, our work examines how gender is encoded in LMs and provides a simple yet effective approach toward controlled gender intervention for both neuron intervention evaluation and gender bias mitigation. Code and datasets are available at: https://github.com/zhiwenyou103/Gender-Neuron-Intervention
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