arXiv：cs.AI· Luc Hazenoot, Zhaochun Ren, Amirhossein Zohrehvand·· 6 小时前
用 LLM 激活值测量文本概念含量：ESG 场景下的概念向量与线性探针对比
Measuring Concept Content in Text from LLM Activations: ESG Evidence from Concept Vectors and Linear Probes
AI 导读
研究比较了 RFM 概念向量与线性探针从 LLM 激活值中提取概念的效果，在人工标注的 ESG 金融文本数据集上，最佳线性探针准确率仅比微调领域分类器低 0.6 至 3.3 个百分点，并在所有对比中超过模型自身回答和 RFM 概念向量。合成混合测试中，线性探针的均值池化得分比概念向量得分更好地跟踪段落中概念句子的数量，表明在冻结 LLM 上使用线性探针是微调领域分类器的高性价比替代方案。
正文
Abstract:Recent work shows that Large Language Models (LLMs) encode more information in their activations than they show in their responses. We ask whether the information contained in activations could be used as a source of measurement for the presence of concepts. We compare two algorithms for extraction of concepts, i.e., Recursive Feature Machine (RFM) algorithm and linear probing against an embedding baseline, surface baselines, and the model's own response using a human-annotated Environmental, Social and Governance (ESG) dataset of financial texts. The best linear probe ranges from 0.6 to 3.3 percentage points below fine-tuned domain classifiers. The probes also outscore the model's own response in all comparisons. Furthermore, the probes outperform the RFM concept vectors in all accuracy comparisons. In a synthetic mixture test, their mean-pooled score tracks the number of concept sentences in a paragraph better than the concept-vector score. Put together, a linear probe on a frozen LLM is a cost-effective and generalizable alternative to fine-tuning a domain classifier.
| Comments: | 26 pages, 3 figures, 16 tables |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); General Economics (econ.GN) |
| ACM classes: | I.2.7; I.2.6; J.4 |
| Cite as: | arXiv:2608.07208 [cs.CL] |
| (or arXiv:2608.07208v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.07208 arXiv-issued DOI via DataCite |
Submission history
From: Amirhossein Zohrehvand [view email]
[v1]
Fri, 7 Aug 2026 13:21:50 UTC (106 KB)
[v2]
Sun, 11 Oct 2026 20:23:01 UTC (86 KB)
来源：arXiv：cs.AI · arxiv.org