| Challenge: | Existing n-gram based QA metrics have a number of drawbacks and are not suitable for all extractive tasks. |
| Approach: | They propose to use BERTScore to evaluate translation for question answering (QA) they also explore whether existing n-gram based metrics are suitable for generative QA . |
| Outcome: | The proposed BERTScore metric fails to provide stronger correlation with human judgements . |
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| Challenge: | Current short-form QA evaluations lack diverse styles of evaluation data and rely on expensive and slow LLMs. |
| Approach: | They propose a rubric for machine QA that is more stable than an exact match and neural methods. |
| Outcome: | The proposed evaluations improve on the existing short-form QA evaluations using the Trivia community. |
Revisiting Evaluation of Question Answering Systems in Low-Resource Indic Languages: Bridging Human and Metric Alignment (2026.acl-short)
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| Challenge: | Evaluating Question Answering systems in low-resource Indic languages remains challenging due to the scarcity of annotated data and the lack of reliable evaluation metrics. |
| Approach: | They propose a language-based multi-aspect evaluation framework for question answering systems . the framework integrates semantic similarity, factual completeness, numerical accuracy and contextual relevance . |
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Towards a Better Metric for Evaluating Question Generation Systems (D18-1)
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| Challenge: | Existing evaluation metrics based on n-gram similarity do not correlate well with human judgments . large datasets for document Question Answering (QA) have enabled the development of end-to-end supervised models . |
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Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation (2022.emnlp-main)
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| Challenge: | despite the importance of question answering, evaluations of QA systems are typically limited by manual annotations . despite this, little progress has been made in QA evaluations based on a single answer . |
| Approach: | They propose to extend over exact match (EM) with predefined rules or token-level F1 measure . they propose to use a BERT matching measure to approximate QA predictions . |
| Outcome: | The proposed model improves AE approximations and more accurately reflects the performance of systems. |
Multi-Layered Evaluation Using a Fusion of Metrics and LLMs as Judges in Open-Domain Question Answering (2025.coling-main)
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| Challenge: | Existing methods for comparing machine-generated answers with reference are not perfect in terms of accuracy or cost. |
| Approach: | They propose to summarize long answers and use shortened versions to improve evaluation . they propose a multi-layered evaluation methodology that integrates different metrics tailored to various scenarios . |
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What do Models Learn from Question Answering Datasets? (2020.emnlp-main)
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| Challenge: | Existing models have outperformed humans on question answering datasets, but they have yet to outperform humans on the task of question answering itself. |
| Approach: | They evaluate BERT-based question answering models on their generalizability to out-of-domain examples, responses to missing or incorrect data, and ability to handle question variations. |
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RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question (2023.findings-acl)
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Alireza Mohammadshahi, Thomas Scialom, Majid Yazdani, Pouya Yanki, Angela Fan, James Henderson, Marzieh Saeidi
| Challenge: | Existing metrics for evaluating the quality of automatically generated questions are expensive and penalise valid questions that may not have high lexical or semantic similarity to the reference questions. |
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Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors (2021.emnlp-main)
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| Challenge: | Evaluation metrics are a key ingredient for progress of text generation systems . a class of novel evaluation metrics based on BERT and its variants has been explored . |
| Approach: | They propose to disentangle BERT-based evaluation metrics along linguistic factors . they show they are sensitive to lexical overlap, just like BLEU and ROUGE . |
| Outcome: | The proposed metrics capture all aspects but are sensitive to lexical overlap, just like BLEU and ROUGE, the authors show . |
NoiseQA: Challenge Set Evaluation for User-Centric Question Answering (2021.eacl-main)
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| Challenge: | Question-Answering (QA) systems are deployed in the real world . a lack of research attention has been devoted to studying the issues that arise when people use QA systems. |
| Approach: | They show that component components that precede an answering engine can introduce varied and considerable sources of error. |
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Benchmarking Answer Verification Methods for Question Answering-Based Summarization Evaluation Metrics (2022.findings-acl)
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| Challenge: | Existing QA-based summarization metrics must automatically determine whether the QA model’s prediction is correct or not. |
| Approach: | They benchmark lexical answer verification methods used by current QA-based metrics and two more sophisticated text comparison methods, BERTScore and LERC. |
| Outcome: | The proposed methods outperform the other methods in some settings while remaining statistically indistinguishable from lexical overlap in others. |