mlsanityData to 7 Oct 2026

Fine-Tuning Language Models to Know What They Know

arXiv 2602.02605 ↗First posted 2 Feb 2026cs.NE cs.AI cs.CLcited by 2

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Abstract (arXiv)

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. Finally, parameter analysis reveals that these improvements are driven by a sparse set of parameters, offering new pathways for targeted metacognitive optimization.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 36 of its 73 references that are in the map. First posted on arXiv 2 Feb 2026.

Builds on

36 of 73 references.

References · 36

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2017On Calibration of Modern Neural Networkscited by 4,860
  3. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  4. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
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  6. 2020Shortcut Learning in Deep Neural Networkscited by 1,928
  7. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  8. 2022Language Models (Mostly) Know What They Knowcited by 1,783
  9. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,081
  10. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 946
  11. 2019Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inferencecited by 864
  12. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
24 more
  1. 2022Teaching Models to Express Their Uncertainty in Wordscited by 731
  2. 2023Do Large Language Models Know What They Don't Know?cited by 238
  3. 2020MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answeringcited by 151
  4. 2024ProSA: Assessing and Understanding the Prompt Sensitivity of LLMscited by 145
  5. 2023Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Modelscited by 117
  6. 2024Know Your Limits: A Survey of Abstention in Large Language Modelscited by 116
  7. 2023R-Tuning: Instructing Large Language Models to Say `I Don't Know'cited by 106
  8. 2024Looking Inward: Language Models Can Learn About Themselves by Introspectioncited by 105
  9. 2024Reducing hallucination in structured outputs via Retrieval-Augmented Generationcited by 92
  10. 2025Tell me about yourself: LLMs are aware of their learned behaviorscited by 86
  11. 2024Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solvingcited by 82
  12. 2023Metacognitive Prompting Improves Understanding in Large Language Modelscited by 58
  13. 2025Language Models Are Capable of Metacognitive Monitoring and Control of Their Internal Activationscited by 37
  14. 2025Adaptive Tool Use in Large Language Models with Meta-Cognition Triggercited by 29
  15. 2025Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learningcited by 28
  16. 2024Metacognitive Retrieval-Augmented Large Language Modelscited by 26
  17. 2025Metacognition and Uncertainty Communication in Humans and Large Language Modelscited by 23
  18. 2025Does It Make Sense to Speak of Introspection in Large Language Models?cited by 22
  19. 2025Evolution Strategies at the Hyperscalecited by 22
  20. 2025Auditing Meta-Cognitive Hallucinations in Reasoning Large Language Modelscited by 19
  21. 2025MetaFaith: Faithful Natural Language Uncertainty Expression in LLMscited by 15
  22. 2025Do Large Language Models Know How Much They Know?cited by 11
  23. 2025Large Language Models Have Intrinsic Meta-Cognition, but Need a Good Lenscited by 10
  24. 2025Line of Duty: Evaluating LLM Self-Knowledge via Consistency in Feasibility Boundariescited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Metacognition in LLMs: Foundations, Progress, and Opportunitiescited by 1
  2. 2026The Dunning-Kruger Effect in Large Language Models: An Empirical Study of Confidence Calibrationcited by 0