用转换编码器分析大模型欺骗行为，发现可定位相关特征。
Transcoders for Investigating Deception in Language Models
- 通过分层转换编码器构建特征激活图，实现电路级行为分析。
- 识别出一组欺骗相关特征，其变化能稳定引导模型生成欺骗性回答。
- 适用于安全研究者检测模型潜在恶意行为，助力风险预警。
转换编码器（transcoders）是机制可解释性领域的新方法，可实现模型行为的电路级分析。本文利用预训练的分层转换编码器（PLTs）对 Qwen3-4B 模型进行分析，构建了捕捉特征激活与特征间依赖关系的归因图，进而开展欺骗行为的电路级研究。通过特征操控与电路分析，我们识别出一组与欺骗相关的特征，并证明这些特征对生成欺骗性输出具有更强影响力——其激活状态的变化可引发可预测的欺骗/非欺骗响应切换。结果表明，欺骗行为源于模型内部机制，凸显了转换编码器在行为监控与安全漏洞早期检测中的潜力。
原文摘要 · Abstract (English)
Transcoders have recently emerged as a promising approach for mechanistic interpretability (MI), enabling circuit-level analysis of model behaviour. In this paper, we investigate the use of transcoders to analyse deceptive behaviour in language models, a behaviour that poses a safety and security risk. Using a Qwen3-4B model with pre-trained transcoders, specifically per-layer transcoders (PLTs), we construct attribution graphs that capture feature activations and inter-feature dependencies, allowing circuit-level analysis of deception. Through feature steering and circuit analysis, we identified a dictionary of deception-related features and show that these features exert a stronger influence on deceptive outputs, as they produce predictable shifts between deceptive and non-deceptive responses. These findings suggest that deception emerges from internal model mechanisms and highlight the potential of transcoders for behavioural monitoring and early detection of security vulnerabilities related to malicious behaviours in language models.
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