Calibrating LLM Confidence by Probing Perturbed Representation Stability

• URL: http://arxiv.org/abs/2505.21772v1
• Date: Tue, 27 May 2025 21:14:04 GMT
• Title: Calibrating LLM Confidence by Probing Perturbed Representation Stability
• Authors: Reza Khanmohammadi, Erfan Miahi, Mehrsa Mardikoraem, Simerjot Kaur, Ivan Brugere, Charese H. Smiley, Kundan Thind, Mohammad M. Ghassemi,
• Abstract summary: Miscalibration in Large Language Models (LLMs) undermines their reliability, highlighting the need for accurate confidence estimation.<n>We introduce CCPS, a novel method analyzing internal representational stability in LLMs.<n>We show that CCPS reduces Expected Error by approximately 55% and Brier-Pro benchmarks by 21%, while increasing accuracy by 5 percentage points.
• Score: 2.2289267617545616
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Miscalibration in Large Language Models (LLMs) undermines their reliability, highlighting the need for accurate confidence estimation. We introduce CCPS (Calibrating LLM Confidence by Probing Perturbed Representation Stability), a novel method analyzing internal representational stability in LLMs. CCPS applies targeted adversarial perturbations to final hidden states, extracts features reflecting the model's response to these perturbations, and uses a lightweight classifier to predict answer correctness. CCPS was evaluated on LLMs from 8B to 32B parameters (covering Llama, Qwen, and Mistral architectures) using MMLU and MMLU-Pro benchmarks in both multiple-choice and open-ended formats. Our results show that CCPS significantly outperforms current approaches. Across four LLMs and three MMLU variants, CCPS reduces Expected Calibration Error by approximately 55% and Brier score by 21%, while increasing accuracy by 5 percentage points, Area Under the Precision-Recall Curve by 4 percentage points, and Area Under the Receiver Operating Characteristic Curve by 6 percentage points, all relative to the strongest prior method. CCPS delivers an efficient, broadly applicable, and more accurate solution for estimating LLM confidence, thereby improving their trustworthiness.

Related papers

• SGIC: A Self-Guided Iterative Calibration Framework for RAG [45.17496149653415]
  Large language models (LLMs) capitalize on their robust in-context reasoning.<n>We present a new framework that employs uncertainty scores as a tool.<n>We also introduce an innovative approach for constructing an iterative self-calibration training set.
  arXiv  Detail & Related papers  (2025-06-19T09:45:13Z)
• Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception [58.62352010928591]
  Large language models (LLMs) exhibit impressive performance across diverse tasks but often struggle to accurately gauge their knowledge boundaries.<n>This paper explores leveraging LLMs' internal states to enhance their perception of knowledge boundaries from efficiency and risk perspectives.
  arXiv  Detail & Related papers  (2025-02-17T11:11:09Z)
• Confident or Seek Stronger: Exploring Uncertainty-Based On-device LLM Routing From Benchmarking to Generalization [61.02719787737867]
  Large language models (LLMs) are increasingly deployed and democratized on edge devices.<n>One promising solution is uncertainty-based SLM routing, offloading high-stakes queries to stronger LLMs when resulting in low-confidence responses on SLM.<n>We conduct a comprehensive investigation into benchmarking and generalization of uncertainty-driven routing strategies from SLMs to LLMs over 1500+ settings.
  arXiv  Detail & Related papers  (2025-02-06T18:59:11Z)
• Rational Tuning of LLM Cascades via Probabilistic Modeling [0.9208007322096532]
  We present a probabilistic model for the joint performance distribution of a sequence of large language models (LLMs)<n>Compared to selecting confidence thresholds using Bayesian optimization, our Markov parametric-copula model yields more favorable error-cost trade-offs.<n>Our framework's inductive assumptions about the interactions between the error rates of different LLMs enhance sample efficiency.
  arXiv  Detail & Related papers  (2025-01-16T07:58:33Z)
• Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles [4.477423478591491]
  Calib-n is a novel framework that trains an auxiliary model for confidence estimation.<n>We find that few-shot prompts are the most effective for auxiliary model-based methods.
  arXiv  Detail & Related papers  (2025-01-07T18:48:42Z)
• 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)
• Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs [21.94487480599671]
  Calibrated Fine-Tuning (UQ4CT) captures and calibrates uncertainty over the space of functions that map input prompts to outputs.<n>We implement UQ4CT during the fine-tuning stage via a mixture-of-experts framework that hierarchically decomposes the functional space.<n>Even under distribution shift, UQ4CT maintains superior ECE performance with high accuracy, showcasing improved generalizability.
  arXiv  Detail & Related papers  (2024-10-09T00:09:15Z)
• Confidence Estimation for LLM-Based Dialogue State Tracking [9.305763502526833]
  Estimation of a model's confidence on its outputs is critical for Conversational AI systems based on large language models (LLMs) We provide an exhaustive exploration of methods, including approaches proposed for open- and closed-weight LLMs. Our findings suggest that fine-tuning open-weight LLMs can result in enhanced AUC performance, indicating better confidence score calibration.
  arXiv  Detail & Related papers  (2024-09-15T06:44:26Z)
• Cycles of Thought: Measuring LLM Confidence through Stable Explanations [53.15438489398938]
  Large language models (LLMs) can reach and even surpass human-level accuracy on a variety of benchmarks, but their overconfidence in incorrect responses is still a well-documented failure mode. We propose a framework for measuring an LLM's uncertainty with respect to the distribution of generated explanations for an answer.
  arXiv  Detail & Related papers  (2024-06-05T16:35:30Z)
• SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models [63.118592279833656]
  Post-training quantization (PTQ) is an effective technique for compressing large language models (LLMs)<n>We propose SliM-LLM, a salience-driven mixed-precision quantization framework that allocates bit-widths at the group-wise.<n> Experiments show that SliM-LLM achieves superior performance across various LLMs at low bit-widths.
  arXiv  Detail & Related papers  (2024-05-23T16:21:48Z)
• Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models [84.94220787791389]
  We propose Fact-and-Reflection (FaR) prompting, which improves the LLM calibration in two steps. Experiments show that FaR achieves significantly better calibration; it lowers the Expected Error by 23.5%. FaR even elicits the capability of verbally expressing concerns in less confident scenarios.
  arXiv  Detail & Related papers  (2024-02-27T01:37:23Z)
• Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs [60.61002524947733]
  Previous confidence elicitation methods rely on white-box access to internal model information or model fine-tuning. This leads to a growing need to explore the untapped area of black-box approaches for uncertainty estimation. We define a systematic framework with three components: prompting strategies for eliciting verbalized confidence, sampling methods for generating multiple responses, and aggregation techniques for computing consistency.
  arXiv  Detail & Related papers  (2023-06-22T17:31:44Z)

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.