通过探针定位大模型有害行为的源头数据，可有效缓解安全风险。
Probe-Based Data Attribution: Discovering and Mitigating Undesirable Behaviors in LLM Post-Training
- 用激活差异向量匹配行为与训练数据，定位引发特定行为的样本
- 过滤高相关数据使危险响应降低63%，改标签效果达78%
- 比梯度法快10倍以上，适合真实场景中发现隐蔽安全问题
我们提出基于探针的数据归因方法，将后训练语言模型的行为变化追溯到具体的训练数据。通过计算测试提示和偏好对的激活差异向量，并按余弦相似度排序，识别出导致特定行为的数据点；并通过重新训练验证归因的因果性。聚类行为-数据相似矩阵还可实现无监督的新兴行为发现。应用于OLMo 2的生产级DPO训练时，发现了‘干扰触发合规’这一有害行为：当良性格式指令附加时，模型会响应危险请求。过滤排名靠前的数据点使该行为减少63%，更换标签则达成78%的抑制效果。该方法优于基于梯度的归因和LLM裁判基线，且成本低于两者10倍以上。这种源自污染偏好数据而非刻意注入的‘真实世界模型生物’，为安全技术提供了现实基准。
原文摘要 · Abstract (English)
We propose probe-based data attribution, a method that traces behavioral changes in post-trained language models to responsible training datapoints. By computing activation-difference vectors for both test prompts and preference pairs and ranking by cosine similarity, we identify datapoints that cause specific behaviors and validate these attributions causally by retraining with modified data. Clustering behavior-datapoint similarity matrices also enables unsupervised discovery of emergent behaviors. Applying this to OLMo 2's production DPO training, we surfaced distractor-triggered compliance: a harmful behavior where the model complies with dangerous requests when benign formatting instructions are appended. Filtering top-ranked datapoints reduces this behavior by 63% while switching their labels achieves 78%. Our method outperforms gradient-based attribution and LLM-judge baselines while being over 10 times cheaper than both. This in-the-wild model organism - emerging from contaminated preference data rather than deliberate injection - provides a realistic benchmark for safety techniques.
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