@inproceedings{zeng-etal-2025-mis,
title = "Mis-prompt: Benchmarking Large Language Models for Proactive Error Handling",
author = "Zeng, Jiayi and
Feng, Yizhe and
He, Mengliang and
Lei, Wenhui and
Zhang, Wei and
Liu, Zeming and
Shi, Xiaoming and
Zhou, Aimin",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.833/",
doi = "10.18653/v1/2025.acl-long.833",
pages = "17007--17034",
ISBN = "979-8-89176-251-0",
abstract = "Large language models (LLMs) have demonstrated significant advancements in error handling. Current error-handling works are performed in a passive manner, with explicit error-handling instructions. However, in real-world scenarios, explicit error-handling instructions are usually unavailable. In this paper, our work identifies this challenge as how to conduct proactive error handling without explicit error handling instructions. To promote further research, this work introduces a new benchmark, termed Mis-prompt, consisting of four evaluation tasks, an error category taxonomy, and a new evaluation dataset. Furthermore, this work analyzes current LLMs' performance on the benchmark, and the experimental results reveal that current LLMs show poor performance on proactive error handling, and SFT on error handling instances improves LLMs' proactive error handling capabilities. The dataset will be publicly available."
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<abstract>Large language models (LLMs) have demonstrated significant advancements in error handling. Current error-handling works are performed in a passive manner, with explicit error-handling instructions. However, in real-world scenarios, explicit error-handling instructions are usually unavailable. In this paper, our work identifies this challenge as how to conduct proactive error handling without explicit error handling instructions. To promote further research, this work introduces a new benchmark, termed Mis-prompt, consisting of four evaluation tasks, an error category taxonomy, and a new evaluation dataset. Furthermore, this work analyzes current LLMs’ performance on the benchmark, and the experimental results reveal that current LLMs show poor performance on proactive error handling, and SFT on error handling instances improves LLMs’ proactive error handling capabilities. The dataset will be publicly available.</abstract>
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%0 Conference Proceedings
%T Mis-prompt: Benchmarking Large Language Models for Proactive Error Handling
%A Zeng, Jiayi
%A Feng, Yizhe
%A He, Mengliang
%A Lei, Wenhui
%A Zhang, Wei
%A Liu, Zeming
%A Shi, Xiaoming
%A Zhou, Aimin
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zeng-etal-2025-mis
%X Large language models (LLMs) have demonstrated significant advancements in error handling. Current error-handling works are performed in a passive manner, with explicit error-handling instructions. However, in real-world scenarios, explicit error-handling instructions are usually unavailable. In this paper, our work identifies this challenge as how to conduct proactive error handling without explicit error handling instructions. To promote further research, this work introduces a new benchmark, termed Mis-prompt, consisting of four evaluation tasks, an error category taxonomy, and a new evaluation dataset. Furthermore, this work analyzes current LLMs’ performance on the benchmark, and the experimental results reveal that current LLMs show poor performance on proactive error handling, and SFT on error handling instances improves LLMs’ proactive error handling capabilities. The dataset will be publicly available.
%R 10.18653/v1/2025.acl-long.833
%U https://aclanthology.org/2025.acl-long.833/
%U https://doi.org/10.18653/v1/2025.acl-long.833
%P 17007-17034
Markdown (Informal)
[Mis-prompt: Benchmarking Large Language Models for Proactive Error Handling](https://aclanthology.org/2025.acl-long.833/) (Zeng et al., ACL 2025)
ACL
- Jiayi Zeng, Yizhe Feng, Mengliang He, Wenhui Lei, Wei Zhang, Zeming Liu, Xiaoming Shi, and Aimin Zhou. 2025. Mis-prompt: Benchmarking Large Language Models for Proactive Error Handling. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 17007–17034, Vienna, Austria. Association for Computational Linguistics.