title: Efficient Test-Time Scaling via Self-Calibration
summary: Increasing test-time computation is a straightforward approach to enhancing
  the quality of responses in Large Language Models (LLMs). While Best-of-N
  sampling and Self-Consistency with majority voting are simple and effective,
  they require a fixed number of sampling responses for each query, regardless of
  its complexity. This could result in wasted computation for simpler questions
  and insufficient exploration for more challenging ones. In this work, we argue
  that model confidence of responses can be used for improving the efficiency of
  test-time scaling. Unfortunately, LLMs are known to be overconfident and
  provide unreliable confidence estimation. To address this limitation, we
  introduce Self-Calibration by distilling Self-Consistency-derived confidence
  into the model itself. This enables reliable confidence estimation at test time
  with one forward pass. We then design confidence-based efficient test-time
  scaling methods to handle queries of various difficulty, such as Early-Stopping
  for Best-of-N and Self-Consistency with calibrated confidence. Experiments on
  three LLMs across six datasets demonstrate the effectiveness of our approach.
  Specifically, applying confidence-based Early Stopping to Best-of-N improves
  MathQA accuracy from 81.0 to 83.6 with a sample budget of 16 responses,
  indicating the efficacy of confidence-based sampling strategy at inference
  time.
paper_authors._id: 67c732c14aaf26f75cea0d82
paper_authors.name: Chengsong Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-04T21:15:36.013Z
paper_authors._id: 62ea79dd01ed9b0e8f61ccd3
paper_authors.avatarUrl: /avatars/70af83e0e267be39fcd5f23b85e2dafa.svg
paper_authors.fullname: Chengsong Huang
paper_authors.type: user
paper_authors.user: ChengsongHuang
paper_authors._id: 67c732c14aaf26f75cea0d83
paper_authors.name: Langlin Huang
paper_authors._id: 67c732c14aaf26f75cea0d84
paper_authors.name: Jixuan Leng
paper_authors._id: 67c732c14aaf26f75cea0d85
paper_authors.name: Jiacheng Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-10T06:47:21.573Z
paper_authors._id: 635f46d1928a42bc95cfcf7c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/635f46d1928a42bc95cfcf7c/5KF8aLiDCJdl7B1SdJ-7V.png
paper_authors.fullname: Jiacheng Liu
paper_authors.type: user
paper_authors.user: liujch1998
paper_authors._id: 67c732c14aaf26f75cea0d86
paper_authors.name: Jiaxin Huang
paper_title: Efficient Test-Time Scaling via Self-Calibration
paper_summary: Increasing test-time computation is a straightforward approach to enhancing
  the quality of responses in Large Language Models (LLMs). While Best-of-N
  sampling and Self-Consistency with majority voting are simple and effective,
  they require a fixed number of sampling responses for each query, regardless of
  its complexity. This could result in wasted computation for simpler questions
  and insufficient exploration for more challenging ones. In this work, we argue
  that model confidence of responses can be used for improving the efficiency of
  test-time scaling. Unfortunately, LLMs are known to be overconfident and
  provide unreliable confidence estimation. To address this limitation, we
  introduce Self-Calibration by distilling Self-Consistency-derived confidence
  into the model itself. This enables reliable confidence estimation at test time
  with one forward pass. We then design confidence-based efficient test-time
  scaling methods to handle queries of various difficulty, such as Early-Stopping
  for Best-of-N and Self-Consistency with calibrated confidence. Experiments on
  three LLMs across six datasets demonstrate the effectiveness of our approach.
  Specifically, applying confidence-based Early Stopping to Best-of-N improves
  MathQA accuracy from 81.0 to 83.6 with a sample budget of 16 responses,
  indicating the efficacy of confidence-based sampling strategy at inference
  time.
paper_ai_summary: Self-Calibration enhances the efficiency of test-time scaling in Large Language Models by using model confidence to dynamically adjust sampling strategies.
paper_ai_keywords: Large Language Models (LLMs) Best-of-N sampling Self-Consistency Self-Calibration confidence estimation Early-Stopping MathQA