arXiv cs.AI· Hui Chen, Antoine Didisheim, Mo Pourmohammadi, Luciano Somoza, Hanqing Tian·· 4 小时前AI 评分51
arXiv 论文用稀疏自编码器对 LLM 做金融领域脑扫描
A Financial Brain Scan of the LLM
AI 导读
一篇 arXiv 论文用稀疏自编码器（SAE）对大语言模型做金融领域的内部扫描，将激活映射到可解释概念并实现定向 steering。研究发现提示词诱导偏好会激活大量无关概念，而模型自我生成的解释与其内部特征贡献明显脱节，不可作为推理依据；SAE 还可用于削弱特定经济偏差。
正文 · 原文
Abstract:We use sparse autoencoders (SAEs) to "brain scan" large language models, mapping internal activations to interpretable concepts and enabling targeted "steering" of specific features. We show that prompting models to adopt specific preferences activates many unintended concepts, complicating causal inference. In contrast, SAEs permit smooth, more isolated steering of target concepts. Furthermore, our scans reveal a stark disconnect between the model's internal feature contributions and its self-generated explanations, showing that these explanations are unreliable guides to its reasoning. Finally, SAEs provide a lightweight intervention for existing models, producing interpretable embeddings and allowing researchers to attenuate specific economic biases.
| Comments: | 82 pages |
| Subjects: | General Finance (q-fin.GN); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); General Economics (econ.GN) |
| Cite as: | arXiv:2508.21285 [q-fin.GN] |
| (or arXiv:2508.21285v4 [q-fin.GN] for this version) | |
| https://doi.org/10.48550/arXiv.2508.21285 arXiv-issued DOI via DataCite |
Submission history
From: Hanqing Tian [view email]
[v1]
Fri, 29 Aug 2025 01:12:57 UTC (32,547 KB)
[v2]
Fri, 13 Feb 2026 23:28:19 UTC (32,547 KB)
[v3]
Tue, 3 Nov 2026 04:13:06 UTC (10,862 KB)
[v4]
Wed, 4 Nov 2026 04:06:59 UTC (10,862 KB)
来源：arXiv cs.AI · arxiv.org