Efficient Test-Time Scaling via Self-Calibration

• URL: http://arxiv.org/abs/2503.00031v1
• Date: Tue, 25 Feb 2025 00:21:14 GMT
• Title: Efficient Test-Time Scaling via Self-Calibration
• Authors: Chengsong Huang, Langlin Huang, Jixuan Leng, Jiacheng Liu, Jiaxin Huang,
• Abstract summary: Best-of-N sampling and Self-Consistency with majority voting are simple and effective, but require a fixed number of sampling responses for each query.<n>This could result in wasted computation for simpler questions and insufficient exploration for more challenging ones.<n>We argue that model confidence of responses can be used for improving the efficiency of test-time scaling.
• Score: 18.32718448734639
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Scalable Best-of-N Selection for Large Language Models via Self-Certainty [65.31658824274894]
  Best-of-N selection is a key technique for improving the reasoning performance of Large Language Models.<n>We propose self-certainty, a novel and efficient metric to estimate response quality without requiring external reward models.<n>Our findings establish self-certainty as a practical and efficient way for improving LLM reasoning capabilities.
  arXiv  Detail & Related papers  (2025-02-25T19:08:07Z)
• Confidence Improves Self-Consistency in LLMs [9.764747744761085]
  We introduce Confidence-Informed Self-Consistency (CISC)<n>CISC performs a weighted majority vote based on confidence scores obtained directly from the model.<n>When tested on nine models and four datasets, CISC outperforms self-consistency in nearly all configurations.
  arXiv  Detail & Related papers  (2025-02-10T08:10:29Z)
• On Verbalized Confidence Scores for LLMs [25.160810008907397]
  Uncertainty quantification for large language models (LLMs) can establish more human trust into their responses.<n>This work focuses on asking the LLM itself to verbalize its uncertainty with a confidence score as part of its output tokens.<n>We assess the reliability of verbalized confidence scores with respect to different datasets, models, and prompt methods.
  arXiv  Detail & Related papers  (2024-12-19T11:10:36Z)
• Distribution-Free Calibration of Statistical Confidence Sets [2.283561089098417]
  We introduce two novel methods, TRUST and TRUST++, for calibrating confidence sets to achieve distribution-free conditional coverage. We demonstrate that our methods outperform existing approaches, particularly in small-sample regimes.
  arXiv  Detail & Related papers  (2024-11-28T20:45:59Z)
• Fact-Level Confidence Calibration and Self-Correction [64.40105513819272]
  We propose a Fact-Level framework that calibrates confidence to relevance-weighted correctness at the fact level. We also develop Confidence-Guided Fact-level Self-Correction ($textbfConFix$), which uses high-confidence facts within a response as additional knowledge to improve low-confidence ones.
  arXiv  Detail & Related papers  (2024-11-20T14:15:18Z)
• Graph-based Confidence Calibration for Large Language Models [22.394717844099684]
  We propose a novel method to develop a well-calibrated confidence estimation model. We use a weighted graph to represent the consistency among the large language models' responses to a question. We then train a graph neural network to estimate the probability of correct responses.
  arXiv  Detail & Related papers  (2024-11-03T20:36:44Z)
• Mirror-Consistency: Harnessing Inconsistency in Majority Voting [54.30719306011487]
  We present Mirror-Consistency, an enhancement of the standard Self-Consistency approach. Mirror-Consistency incorporates a'reflective mirror' into the self-ensemble decoding process. We show that Mirror-Consistency yields superior performance in both reasoning accuracy and confidence calibration compared to Self-Consistency.
  arXiv  Detail & Related papers  (2024-10-07T03:41:08Z)
• Reasoning Aware Self-Consistency: Leveraging Reasoning Paths for Efficient LLM Sampling [9.44858963874474]
  Self-Consistency mitigates hallucinations in Large Language Models (LLMs) by sampling multiple reasoning paths.<n>We introduce Reasoning-Aware Self-Consistency (RASC), a novel framework that enhances sampling efficiency and reasoning faithfulness.
  arXiv  Detail & Related papers  (2024-08-30T05:14:59Z)
• A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition [74.79785063365289]
  Existing models for named entity recognition (NER) are mainly based on large-scale labeled datasets. We propose a Confidence-based Partial Label Learning (CPLL) method to integrate the prior confidence (given by annotators) and posterior confidences (learned by models) for crowd-annotated NER.
  arXiv  Detail & Related papers  (2023-05-21T15:31:23Z)
• Fast Entropy-Based Methods of Word-Level Confidence Estimation for End-To-End Automatic Speech Recognition [86.21889574126878]
  We show how per-frame entropy values can be normalized and aggregated to obtain a confidence measure per unit and per word. We evaluate the proposed confidence measures on LibriSpeech test sets, and show that they are up to 2 and 4 times better than confidence estimation based on the maximum per-frame probability.
  arXiv  Detail & Related papers  (2022-12-16T20:27:40Z)
• An evaluation of word-level confidence estimation for end-to-end automatic speech recognition [70.61280174637913]
  We investigate confidence estimation for end-to-end automatic speech recognition (ASR) We provide an extensive benchmark of popular confidence methods on four well-known speech datasets. Our results suggest a strong baseline can be obtained by scaling the logits by a learnt temperature.
  arXiv  Detail & Related papers  (2021-01-14T09:51:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.