模型用上那条信息了吗？反事实训练 CREDIT 提升语言模型自我归因能力
Did the Model Use That? Counterfactual Training Boosts Self-Attribution in Language Models
AI 导读
arXiv 论文提出反事实训练方法 CREDIT，让语言模型在最终答案中标注是否受所提供信息影响，无需人工标注。测试发现 11 个前沿模型大多很少归因有影响的信息，其余则错误归因无影响信息。CREDIT 显著改善 4 个开源模型（含 2 个蒸馏推理模型）的归因表现，优于推理级微调和提示词基线，并能区分因用户建议而改变的答案与模型本就会给出的答案。
正文 · 原文
Abstract:A fundamental challenge for explainable and accountable language models is revealing how external information influences their answers. This paper examines whether models are self-attributive: that is, whether they indicate when -- and only when -- their responses to questions are influenced by information provided alongside the question. Furthermore, we explore how self-attribution can be improved through counterfactual training, which explicitly teaches models to distinguish between answers that do and do not depend on supplied information.
We found that most of the eleven frontier models rarely attribute influential information in their answers, and the rest attribute non-influential information. Training acknowledgment into reasoning does not fix this: the acknowledgment does not reach the final answer.
We introduce CREDIT, which directly supervises final-answer self-attribution with counterfactual labels, without the need for human annotation. CREDIT substantially improves attribution in four open models, including two distilled reasoning models, and outperforms reasoning-level fine-tuning and prompting baselines. Among answers that agree with a user's suggestion, CREDIT distinguishes those changed by the suggestion from those the model would have given anyway. Gains transfer partly to unseen information types and prompts containing sources that support conflicting answers.
| Comments: | 29 pages, 5 figures, 20 tables. Code: this https URL |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.7 |
| Cite as: | arXiv:2610.35295 [cs.CL] |
| (or arXiv:2610.35295v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35295 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zikai Zhou [view email]
[v1]
Mon, 26 Oct 2026 12:03:05 UTC (222 KB)
来源：arXiv cs.AI · arxiv.org