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Source note3 min read

Future Confidence Distillation in Large Language Models

Source

arXiv

arxiv.org/abs/2607.07626v1

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Source

Plain-Language Brief

In this research paper from 2026, Sahil Kale explores the importance of accurate confidence estimation in large language models (LLMs) for various applications like retrieval and tool use. The study focuses on how confidence evolves during the answering process, comparing pre-solution and post-solution confidence estimates. The findings suggest that post-solution confidence is more accurate and discriminative than pre-solution confidence. A new method called 'future confidence distillation' is introduced to train predictors using teacher confidence estimates from post-solution correctness probes, even though it only requires pre-solution representations for inference. The study indicates that this method can significantly improve the reliability of confidence estimation at a lower cost.

What the Document Says

  • The paper discusses the importance of reliable confidence estimation for large language models in confidence-aware systems.
  • It points out that existing approaches overlook how confidence-related information evolves during the answering process.
  • The study compares pre-solution Feeling-of-Knowing (FOK) and post-solution Judgement-of-Learning (JOL) confidence estimates across various LLMs.
  • Post-solution confidence is found to be better calibrated and more discriminative than pre-solution confidence.
  • Linear probes trained on hidden representations recover richer confidence-related information than models that explicitly verbalize it.
  • The paper introduces 'future confidence distillation', a method for training predictors using teacher confidence estimates produced by post-solution correctness probes, even though it only requires pre-solution representations for inference.
  • Distilled predictors are shown to recover much of the calibration improvement achieved by post-solution confidence.
  • The method is highly sample efficient and transfers across datasets within the same domain.
  • The findings demonstrate that confidence-related information evolves during the answering process and can be anticipated before answer generation is complete, enabling more reliable yet low-cost confidence estimation.

Key Concepts Mentioned

  • Large Language Models (LLMs)
  • Confidence Estimation
  • Feeling-of-Knowing (FOK)
  • Judgement-of-Learning (JOL)
  • Future Confidence Distillation

Claims Made by the Document

  • Reliable confidence estimation is essential for deploying large language models in confidence-aware systems.
  • Post-solution confidence is more calibrated and discriminative than pre-solution confidence.
  • Linear probes trained on hidden representations recover richer confidence-related information than models that explicitly verbalize it.
  • Future confidence distillation can improve the reliability of confidence estimation at a lower cost.

Evidence Used

  • Comparisons between pre-solution and post-solution confidence estimates across various LLMs
  • Analyses showing calibration improvement and sample efficiency of future confidence distillation

Methods / Approach

  • Comparison of pre-solution and post-solution confidence estimates
  • Training linear probes on hidden representations
  • Introduction of future confidence distillation

Limitations Stated by the Authors

Not stated in source

Limitations Noted by the Agent

Agent interpretation — not from the source.

The study does not provide any experimental evaluation or specific performance metrics for the introduced 'future confidence distillation' method.

Open Questions

  • What are the performance metrics used to evaluate the effectiveness of future confidence distillation?
  • How does the introduced method compare to existing approaches in real-world applications?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper discusses the importance of reliable confidence estimation for large language models and introduces a new method called 'future confidence distillation'. It demonstrates that post-solution confidence is more accurate than pre-solution confidence, and the new method improves the reliability of confidence estimation at a lower cost. However, it lacks experimental evaluation or specific performance metrics for the introduced method.

Raw Abstract / Excerpt

Reliable confidence estimation is essential for deploying large language models (LLMs) in confidence-aware systems, where downstream decisions such as retrieval, tool use, and adaptive computation depend on accurately estimating answer reliability. Existing approaches, however, largely treat confidence as a property of completed responses, overlooking how confidence-related information evolves throughout the answering process. In this work, we investigate confidence from a temporal perspective by comparing pre-solution Feeling-of-Knowing (FOK) and post-solution Judgement-of-Learning (JOL) confidence estimates across frontier and open-source LLMs. We show that post-solution confidence is consistently better calibrated and more discriminative than pre-solution confidence, while linear probes trained on hidden representations recover substantially richer confidence-related information than models explicitly verbalise. Building on this observation, we introduce future confidence distillation, which trains predictors operating on pre-solution hidden representations using teacher confidence estimates produced by post-solution correctness probes. Despite requiring only pre-solution representations for inference, distilled predictors recover much of the calibration improvement achieved by post-solution confidence, remain highly sample efficient, and transfer across datasets within the same domain. Together, our findings demonstrate that confidence-related information evolves throughout the answering process and can be anticipated before answer generation is complete, enabling significantly more reliable yet low-cost confidence estimation.