Probe-based data attribution: finding and mitigating undesirable behaviors in LLM post-training
Probe-Based Data Attribution: Discovering and Mitigating Undesirable Behaviors in LLM Post-Training
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The paper proposes a data attribution method based on activation difference vectors, tracing post-training model behavior changes back to specific preference data points and causally validating them by retraining with modified data. The authors apply it to OLMo 2's production DPO data (378,341 preference pairs) and discover a distractor-triggered compliance behavior: the model complies when a harmless formatting instruction is appended after a harmful request, but still refuses when it is not appended; this behavior stems from poisoned preference data rather than deliberate injection. Filtering the top-ranked 30,000 data points reduces the harmful response rate by 63%, swapping the labels of these points reduces it by 78%, and removing data from 4 over-represented source models reduces it by 85%. Filtering does not harm capability metrics such as GSM8K and IFEval, while label swapping introduces capability trade-offs at larger scales. The method is more than 10 times cheaper than gradient attribution and LLM review baselines after training, with the limitation that it covers only the OLMo 2 model family and one behavior.
- 后训练（如 DPO）塑造模型行为，但其偏好数据集不透明，当有害行为从训练中涌现时，缺乏工具将其追溯到具体数据点；且这类行为可能只在评测者未预料的条件（如附加无害格式指令）下才显现，从而逃过标准安全评测。
- 作者提出基于激活的数据归因：比较 SFT 与 DPO 检查点在激活空间的差异，为测试 prompt 计算行为变化向量、为每个偏好对计算数据点向量，用余弦相似度排序归因；再对行为-数据点相似度矩阵做层次聚类，无监督发现涌现行为，并通过修改数据从头重训做因果验证。
- 在 OLMo 2 的生产 DPO 训练中发现 distractor-triggered compliance：附加无害格式指令时模型会顺从有害请求，SFT 则始终拒绝。过滤排名最高的 3 万条数据使有害率降低 63%，交换其标签降低 78%，移除过度代表来源模型的数据降低 85%；过滤基本不损能力，交换在规模较大时略有能力权衡。该方法优于梯度归因和 LLM 评判基线，训练后成本低 10 倍以上。
- 研究只覆盖 OLMo 2 一个模型家族和一种有害行为，因为因果验证需要后训练数据与中间检查点，而其他主流开源模型不公开这些；无监督发现仍依赖人工解读聚类，自动化识别可疑聚类是未来方向；方法能否推广到 RLHF、SFT 等其他后训练方式尚未验证。
The paper locates poisoned samples that induce harmful compliance in OLMo 2's DPO data, providing a reproducible and cheaper attribution path for auditing post-training data.