arXiv：cs.LG· Ciar\'an M. Gilligan-Lee, Joseph Egan, Yuchen Zhu, Michael O'Riordan·· 13 小时前
GRASP：微调过程中无监督识别并移除虚假相关
Unsupervised Identification and Removal of Spurious Correlations During Fine-Tuning
AI 导读
针对微调预训练语言模型会引入任务与潜在因子间虚假相关的问题，研究者提出 GRASP（关联虚假模式的梯度投影），无需监督即可从朴素 LoRA 微调权重中识别潜在因子，并阻止模型新增对它的依赖，同时保留预训练内容。在三个微调任务中，不安全代码案例的错位被完全移除，不良医疗建议案例降低约 5 倍，右倾 Reddit 理财数据实验的政治倾向漂移减少一半以上，且任务表现均优于全部基线。
正文
Abstract:Fine-tuning a pretrained language model on a curated dataset can produce spurious correlations between the fine-tuning task and unintended latent factors---such as misaligned personas or political slant---that the curation procedure has entangled with the task. The model can latch onto these spurious correlations, leading to bias and reduced out-of-distribution generalisation. We prove that under reasonable assumptions on task complexity and the spurious correlation, such latent factors can be identified, without supervision, from the weights of a naive LoRA fine-tune. Existing approaches to removing bias, such as activation steering, remove identified factors from residual-stream activations, either at inference or during training. We argue, however, that the goal should be to remove the spurious correlation, not the latent factor itself, as the pretrained model may rely on it for genuine task signal. To enable this, we propose GRASP, GRadient projection of Associated Spurious Patterns, which prevents the model from acquiring new reliance on the identified latent factor while preserving any pretrained content along it. We validate on three fine-tuning tasks. The first two involve emergent misalignment, where fine-tuning on a narrow task---in our case, writing insecure code and giving bad medical advice---leads to misaligned responses on unrelated topics. Here our method completely removes misalignment in the insecure code case and reduces it by ~5x in the bad medical advice case, beating all baselines in the trade-off between misalignment-reduction and task-preservation. The last is a novel political-bias experiment, where fine-tuning on right-skewed Reddit financial-advice data causes political-lean drift on unrelated topics. Here our method reduces drift by more than half, while improving financial task performance, beating all baselines.
| Comments: | 10 + 9 pages, comments welcome |
| Subjects: | Machine Learning (stat.ML); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.27676 [stat.ML] |
| (or arXiv:2605.27676v2 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2605.27676 arXiv-issued DOI via DataCite |
Submission history
From: Ciarán M. Gilligan-Lee [view email]
[v1]
Tue, 26 May 2026 20:51:48 UTC (28 KB)
[v2]
Fri, 9 Oct 2026 12:41:05 UTC (36 KB)
来源：arXiv：cs.LG · arxiv.org