ErrEval: Error-Aware Evaluation for Question Generation through Explicit Diagnostics
This addresses the issue of overestimating question quality in QG evaluation for researchers and practitioners, though it is incremental as it builds on existing LLM-based evaluators.
The paper tackled the problem of automatic Question Generation (QG) producing outputs with critical defects like factual hallucinations, and proposed ErrEval, an error-aware evaluation framework that improves alignment with human judgments by incorporating explicit error diagnostics.
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.