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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.10406 |
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| _version_ | 1866915732528300032 |
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| author | Fu, Weiping Wei, Bifan Hao, Jingyi Zhang, Yushun Zhang, Jian Wang, Jiaxin Li, Bo He, Yu Zhang, Lingling Liu, Jun |
| author_facet | Fu, Weiping Wei, Bifan Hao, Jingyi Zhang, Yushun Zhang, Jian Wang, Jiaxin Li, Bo He, Yu Zhang, Lingling Liu, Jun |
| contents | Automatic Question Generation (QG) often produces outputs with critical defects, such as factual hallucinations and answer mismatches. However, existing evaluation methods, including LLM-based evaluators, mainly adopt a black-box and holistic paradigm without explicit error modeling, leading to the neglect of such defects and overestimation of question quality. To address this issue, we propose ErrEval, a flexible and Error-aware Evaluation framework that enhances QG evaluation through explicit error diagnostics. Specifically, ErrEval reformulates evaluation as a two-stage process of error diagnosis followed by informed scoring. At the first stage, a lightweight plug-and-play Error Identifier detects and categorizes common errors across structural, linguistic, and content-related aspects. These diagnostic signals are then incorporated as explicit evidence to guide LLM evaluators toward more fine-grained and grounded judgments. Extensive experiments on three benchmarks demonstrate the effectiveness of ErrEval, showing that incorporating explicit diagnostics improves alignment with human judgments. Further analyses confirm that ErrEval effectively mitigates the overestimation of low-quality questions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_10406 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ErrEval: Error-Aware Evaluation for Question Generation through Explicit Diagnostics Fu, Weiping Wei, Bifan Hao, Jingyi Zhang, Yushun Zhang, Jian Wang, Jiaxin Li, Bo He, Yu Zhang, Lingling Liu, Jun Artificial Intelligence Automatic Question Generation (QG) often produces outputs with critical defects, such as factual hallucinations and answer mismatches. However, existing evaluation methods, including LLM-based evaluators, mainly adopt a black-box and holistic paradigm without explicit error modeling, leading to the neglect of such defects and overestimation of question quality. To address this issue, we propose ErrEval, a flexible and Error-aware Evaluation framework that enhances QG evaluation through explicit error diagnostics. Specifically, ErrEval reformulates evaluation as a two-stage process of error diagnosis followed by informed scoring. At the first stage, a lightweight plug-and-play Error Identifier detects and categorizes common errors across structural, linguistic, and content-related aspects. These diagnostic signals are then incorporated as explicit evidence to guide LLM evaluators toward more fine-grained and grounded judgments. Extensive experiments on three benchmarks demonstrate the effectiveness of ErrEval, showing that incorporating explicit diagnostics improves alignment with human judgments. Further analyses confirm that ErrEval effectively mitigates the overestimation of low-quality questions. |
| title | ErrEval: Error-Aware Evaluation for Question Generation through Explicit Diagnostics |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.10406 |