@inproceedings{pan-etal-2025-mpbench,
title = "{MPB}ench: A Comprehensive Multimodal Reasoning Benchmark for Process Errors Identification",
author = "Pan, xu Zhao and
Zhou, Pengfei and
Ai, Jiaxin and
Zhao, Wangbo and
Wang, Kai and
Peng, Xiaojiang and
Shao, Wenqi and
Yao, Hongxun and
Zhang, Kaipeng",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1112/",
doi = "10.18653/v1/2025.findings-acl.1112",
pages = "21586--21606",
ISBN = "979-8-89176-256-5",
abstract = "Reasoning is an essential capacity for large language models (LLMs) to address complex tasks, whereas the identification of process errors is vital for improving this ability. Recently, process-level reward models (PRMs) were proposed to provide step-wise rewards that facilitate reinforcement learning and data production during training and guide LLMs toward correct steps during inference, thereby improving reasoning accuracy. However, existing benchmarks of PRMs are text-based and focus on error detection, neglecting other scenarios like reasoning search. To address this gap, we introduce MPBench, a comprehensive, multi-task, multimodal benchmark designed to systematically assess the effectiveness of PRMs in diverse scenarios. MPBench employs three evaluation paradigms, each targeting a specific role of PRMs in the reasoning process: (1) Step Correctness, which assesses the correctness of each intermediate reasoning step; (2) Answers Aggregation, which aggregates multiple solutions and selects the best one; and (3) Reasoning Process Search, which guides the search for optimal reasoning steps during inference. Through these paradigms, MPBench makes comprehensive evaluations and provides insights into the development of multimodal PRMs."
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<abstract>Reasoning is an essential capacity for large language models (LLMs) to address complex tasks, whereas the identification of process errors is vital for improving this ability. Recently, process-level reward models (PRMs) were proposed to provide step-wise rewards that facilitate reinforcement learning and data production during training and guide LLMs toward correct steps during inference, thereby improving reasoning accuracy. However, existing benchmarks of PRMs are text-based and focus on error detection, neglecting other scenarios like reasoning search. To address this gap, we introduce MPBench, a comprehensive, multi-task, multimodal benchmark designed to systematically assess the effectiveness of PRMs in diverse scenarios. MPBench employs three evaluation paradigms, each targeting a specific role of PRMs in the reasoning process: (1) Step Correctness, which assesses the correctness of each intermediate reasoning step; (2) Answers Aggregation, which aggregates multiple solutions and selects the best one; and (3) Reasoning Process Search, which guides the search for optimal reasoning steps during inference. Through these paradigms, MPBench makes comprehensive evaluations and provides insights into the development of multimodal PRMs.</abstract>
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%0 Conference Proceedings
%T MPBench: A Comprehensive Multimodal Reasoning Benchmark for Process Errors Identification
%A Pan, xu Zhao
%A Zhou, Pengfei
%A Ai, Jiaxin
%A Zhao, Wangbo
%A Wang, Kai
%A Peng, Xiaojiang
%A Shao, Wenqi
%A Yao, Hongxun
%A Zhang, Kaipeng
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F pan-etal-2025-mpbench
%X Reasoning is an essential capacity for large language models (LLMs) to address complex tasks, whereas the identification of process errors is vital for improving this ability. Recently, process-level reward models (PRMs) were proposed to provide step-wise rewards that facilitate reinforcement learning and data production during training and guide LLMs toward correct steps during inference, thereby improving reasoning accuracy. However, existing benchmarks of PRMs are text-based and focus on error detection, neglecting other scenarios like reasoning search. To address this gap, we introduce MPBench, a comprehensive, multi-task, multimodal benchmark designed to systematically assess the effectiveness of PRMs in diverse scenarios. MPBench employs three evaluation paradigms, each targeting a specific role of PRMs in the reasoning process: (1) Step Correctness, which assesses the correctness of each intermediate reasoning step; (2) Answers Aggregation, which aggregates multiple solutions and selects the best one; and (3) Reasoning Process Search, which guides the search for optimal reasoning steps during inference. Through these paradigms, MPBench makes comprehensive evaluations and provides insights into the development of multimodal PRMs.
%R 10.18653/v1/2025.findings-acl.1112
%U https://aclanthology.org/2025.findings-acl.1112/
%U https://doi.org/10.18653/v1/2025.findings-acl.1112
%P 21586-21606
Markdown (Informal)
[MPBench: A Comprehensive Multimodal Reasoning Benchmark for Process Errors Identification](https://aclanthology.org/2025.findings-acl.1112/) (Pan et al., Findings 2025)
ACL
- xu Zhao Pan, Pengfei Zhou, Jiaxin Ai, Wangbo Zhao, Kai Wang, Xiaojiang Peng, Wenqi Shao, Hongxun Yao, and Kaipeng Zhang. 2025. MPBench: A Comprehensive Multimodal Reasoning Benchmark for Process Errors Identification. In Findings of the Association for Computational Linguistics: ACL 2025, pages 21586–21606, Vienna, Austria. Association for Computational Linguistics.