arXiv cs.AI· Sangjun Park, Elliot Meyerson, Xin Qiu, Risto Miikkulainen·· 1 天前AI 评分38
用微调让 LLM 知道自己知道什么：ESMA 方法提升元认知能力
Fine-Tuning Language Models to Know What They Know
AI 导读
研究提出 ESMA（Evolution Strategy for Metacognitive Alignment）方法，通过微调提升 LLM 的元认知能力，并用 $d'_{\rm type2}$ 指标在控制偏差与启发式的前提下单独测量该能力。
正文 · 原文
Abstract:Evaluating true metacognition in Large Language Models (LLMs) is difficult due to biases and heuristics. This paper presents a framework to measure and enhance LLM metacognition while controlling for these biases. A measurement method using the $d'_{\rm type2}$ metric is established to isolate metacognitive ability. The Evolution Strategy for Metacognitive Alignment (ESMA) is proposed, demonstrating robust generalization across unseen datasets, languages, and newly acquired knowledge. Gradual parameter updates reveal that these improvements are driven by a sparse set of parameters, opening a potential avenue for targeted metacognitive optimization.
| Comments: | Accepted to NeurIPS 2026 |
| Subjects: | Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Neurons and Cognition (q-bio.NC) |
| Cite as: | arXiv:2602.02605 [cs.NE] |
| (or arXiv:2602.02605v3 [cs.NE] for this version) | |
| https://doi.org/10.48550/arXiv.2602.02605 arXiv-issued DOI via DataCite |
Submission history
From: Sangjun Park [view email]
[v1]
Mon, 2 Feb 2026 04:08:13 UTC (1,441 KB)
[v2]
Sun, 24 May 2026 06:55:56 UTC (1,449 KB)
[v3]
Tue, 10 Nov 2026 20:13:08 UTC (1,462 KB)
来源：arXiv cs.AI · arxiv.org