ConfProBench: A Confidence Evaluation Benchmark for MLLM-Based Process Judges

• URL: http://arxiv.org/abs/2508.04576v1
• Date: Wed, 06 Aug 2025 16:00:19 GMT
• Title: ConfProBench: A Confidence Evaluation Benchmark for MLLM-Based Process Judges
• Authors: Yue Zhou, Yi Chang, Yuan Wu,
• Abstract summary: We evaluate 14 state-of-the-art MLLMs, including both proprietary and open-source models.<n>We propose ConfProBench, the first comprehensive benchmark designed to systematically evaluate the reliability of step-level confidence scores generated by MPJs.
• Score: 15.47711837051754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reasoning is a critical capability of multimodal large language models (MLLMs) for solving complex multimodal tasks, and judging the correctness of reasoning steps is crucial for improving this capability. Recently, MLLM-based process judges (MPJs) have been widely used to assess the correctness of reasoning steps in multimodal tasks. Therefore, evaluating MPJs is important for identifying their limitations and guiding future improvements. However, existing benchmarks for MPJs mainly focus on tasks such as step correctness classification and reasoning process search, while overlooking a key aspect: whether the confidence scores produced by MPJs at the step level are reliable. To address this gap, we propose ConfProBench, the first comprehensive benchmark designed to systematically evaluate the reliability of step-level confidence scores generated by MPJs. Our benchmark constructs three types of adversarially perturbed reasoning steps: Synonym Substitution, Syntactic Transformation, and Image Perturbation, to test the robustness of MPJ confidence under perturbations. In addition, we introduce three novel evaluation metrics: Confidence Robustness Score (CRS), Confidence Sensitivity Score (CSS), and Confidence Calibration Score (CCS), which evaluate robustness, sensitivity, and calibration, respectively. We evaluate 14 state-of-the-art MLLMs, including both proprietary and open-source models. Experiments reveal limitations in current MPJs' confidence performance and offer competitive baselines to support future research.

Related papers

• MMBoundary: Advancing MLLM Knowledge Boundary Awareness through Reasoning Step Confidence Calibration [2.1824579248418017]
  We present MMBoundary, a novel framework that advances the knowledge boundary awareness of MLLMs through reasoning step confidence calibration.<n>In addition to supervised fine-tuning, we introduce a reinforcement learning stage with multiple reward functions for further aligning model knowledge.<n> Empirical results show that MMBoundary significantly outperforms existing methods across diverse domain datasets and metrics.
  arXiv  Detail & Related papers  (2025-05-29T08:14:40Z)
• ProJudge: A Multi-Modal Multi-Discipline Benchmark and Instruction-Tuning Dataset for MLLM-based Process Judges [13.957207630090064]
  We introduce ProJudgeBench, the first benchmark specifically designed for evaluating abilities of MLLM-based process judges.<n>ProJudgeBench comprises 2,400 test cases and 50,118 step-level labels, spanning four scientific disciplines.<n> Evaluation on ProJudgeBench reveals a significant performance gap between open-source and proprietary models.
  arXiv  Detail & Related papers  (2025-03-09T10:55:51Z)
• SteerConf: Steering LLMs for Confidence Elicitation [11.872504642312705]
  Large Language Models (LLMs) exhibit impressive performance across diverse domains but often suffer from overconfidence.<n>We propose SteerConf, a novel framework that systematically steers LLMs' confidence scores to improve their calibration and reliability.
  arXiv  Detail & Related papers  (2025-03-04T18:40:49Z)
• MCQA-Eval: Efficient Confidence Evaluation in NLG with Gold-Standard Correctness Labels [16.300463494913593]
  Large Language Models (LLMs) require robust confidence estimation.<n>McQCA-Eval is an evaluation framework for assessing confidence measures in Natural Language Generation.
  arXiv  Detail & Related papers  (2025-02-20T05:09:29Z)
• How Reliable are LLMs as Knowledge Bases? Re-thinking Facutality and Consistency [60.25969380388974]
  Large Language Models (LLMs) are increasingly explored as knowledge bases (KBs)<n>Current evaluation methods focus too narrowly on knowledge retention, overlooking other crucial criteria for reliable performance.<n>We propose new criteria and metrics to quantify factuality and consistency, leading to a final reliability score.
  arXiv  Detail & Related papers  (2024-07-18T15:20:18Z)
• MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs [55.20845457594977]
  Large language models (LLMs) have shown increasing capability in problem-solving and decision-making.<n>We present a process-based benchmark MR-Ben that demands a meta-reasoning skill.<n>Our meta-reasoning paradigm is especially suited for system-2 slow thinking.
  arXiv  Detail & Related papers  (2024-06-20T03:50:23Z)
• UBench: Benchmarking Uncertainty in Large Language Models with Multiple Choice Questions [10.28688988951815]
  We introduce UBench, a new benchmark for evaluating the uncertainty of large language models (LLMs)<n>Unlike other benchmarks, UBench is based on confidence intervals. It encompasses 11,978 multiple-choice questions spanning knowledge, language, understanding, and reasoning capabilities.<n>Our analysis reveals several crucial insights: 1) Our confidence interval-based methods are highly effective for uncertainty quantification; 2) Regarding uncertainty, outstanding open-source models show competitive performance versus closed-source models; 3) CoT and RP prompts present potential ways to improve model reliability, while the influence of temperature changes follows no universal rule.
  arXiv  Detail & Related papers  (2024-06-18T16:50:38Z)
• 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)
• When to Trust LLMs: Aligning Confidence with Response Quality [49.371218210305656]
  We propose CONfidence-Quality-ORDer-preserving alignment approach (CONQORD) It integrates quality reward and order-preserving alignment reward functions. Experiments demonstrate that CONQORD significantly improves the alignment performance between confidence and response accuracy.
  arXiv  Detail & Related papers  (2024-04-26T09:42:46Z)
• Think Twice Before Trusting: Self-Detection for Large Language Models through Comprehensive Answer Reflection [90.71323430635593]
  We propose a novel self-detection paradigm that considers the comprehensive answer space beyond LLM-generated answers. Building upon this paradigm, we introduce a two-step framework, which firstly instructs LLM to reflect and provide justifications for each candidate answer. This framework can be seamlessly integrated with existing approaches for superior self-detection.
  arXiv  Detail & Related papers  (2024-03-15T02:38:26Z)
• TrustScore: Reference-Free Evaluation of LLM Response Trustworthiness [58.721012475577716]
  Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, prompting a surge in their practical applications. This paper introduces TrustScore, a framework based on the concept of Behavioral Consistency, which evaluates whether an LLMs response aligns with its intrinsic knowledge.
  arXiv  Detail & Related papers  (2024-02-19T21:12:14Z)
• Self-Evaluation Improves Selective Generation in Large Language Models [54.003992911447696]
  We reformulate open-ended generation tasks into token-level prediction tasks. We instruct an LLM to self-evaluate its answers. We benchmark a range of scoring methods based on self-evaluation.
  arXiv  Detail & Related papers  (2023-12-14T19:09:22Z)

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.