| Challenge: | a dataset is proposed for question answering models with respect to their capacity to reason about beliefs. |
| Approach: | They propose a dataset for evaluating question answering models with respect to their capacity to reason about beliefs. |
| Outcome: | The proposed dataset is inspired by theory-of-mind experiments that examine whether children are able to reason about beliefs of others. |
Similar Papers
Revisiting the Evaluation of Theory of Mind through Question Answering (D19-1)
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| Challenge: | Existing benchmarks for theory of mind are flawed due to dataset biases . evaluators have been using the Sally-Anne test to infer false beliefs in others . |
| Approach: | They propose to use question answering to evaluate theory of mind . they propose to explicitly control for data regularities via a careful examination of the answer space . |
| Outcome: | The proposed evaluation protocol and dataset control for data regularities via a careful examination of the answer space. |
Are Red Roses Red? Evaluating Consistency of Question-Answering Models (P19-1)
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| Challenge: | Existing question-answering systems are limited in their ability to test reasoning and comprehension. |
| Approach: | They propose a method to automatically extract implications from QA datasets to evaluate models' consistency . they use a heuristic to generate such questions and retrain models with implication-augmented data . |
| Outcome: | The proposed method shows that generated implications are well formed and valid . retraining with implication-augmented data improves consistency on both synthetic and human-generated implications. |
Theory of Mind in Large Language Models: Assessment and Enhancement (2025.acl-long)
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| Challenge: | Theory of Mind (ToM) is a cornerstone of human social intelligence . Large Language Models (LLMs) are increasingly integrated into daily life . |
| Approach: | They analyze evaluation benchmarks and enhancement strategies to evaluate LLMs' ToM capabilities. |
| Outcome: | The proposed and widely used story-based benchmarks and enhancement strategies are used to evaluate LLMs' ToM capabilities. |
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. |
| Outcome: | The proposed models outperform human baselines on the widely-used SQuAD 1.1 and SQu AD 2.0 datasets. |
Bend but Don’t Break? Multi-Challenge Stress Test for QA Models (D19-58)
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| Challenge: | a gap remains in reasoning ability compared to a human, and performance tends to degrade when models are exposed to less-constrained tasks. |
| Approach: | They conduct extensive qualitative and quantitative analyses on the results of four models across four datasets . they relate common errors to model capabilities and discuss a way forward . |
| Outcome: | The proposed model performance is based on the results of four models across four datasets. |
DebateQA: Evaluating Question Answering on Debatable Knowledge (2026.findings-eacl)
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| Challenge: | Existing QA benchmarks that provide fixed answers to debatable questions are inadequate for evaluating their performance. |
| Approach: | They propose to use a dataset of 2,941 debatable questions to assess their ability to provide comprehensive answers to inherently debatably asked questions. |
| Outcome: | The proposed model performs well on 2,941 debatable questions accompanied by human-annotated partial answers that capture a variety of perspectives. |
Knowing More About Questions Can Help: Improving Calibration in Question Answering (2021.findings-acl)
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| Challenge: | Existing work on calibration focuses on model confidence, such as the max probability of the predicted class. |
| Approach: | They propose a calibration method which estimates whether model correctly predicts answer for each question. |
| Outcome: | The proposed calibration method achieves 5-10% gains on reading comprehension benchmarks. |
PEDANTS: Cheap but Effective and Interpretable Answer Equivalence (2024.findings-emnlp)
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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. |
Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering (2020.coling-main)
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| Challenge: | Interpretability and explainability of deep neural net models are always challenging due to their size and complexity. |
| Approach: | They propose to design an explainable, evidence-based memory network architecture that connects current sample with seen samples and bases its decision on these samples. |
| Outcome: | The proposed model can trace errors to training instances that might have caused errors . the proposed model achieves state-of-the-art performance on two popular datasets . |
Evaluating Reasoning Models for Queries with Presuppositions (2026.findings-acl)
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| Challenge: | Prior work notes that large language models fail to challenge erroneous assumptions and can reinforce users’ misinformed opinions. |
| Approach: | They construct queries with varying degrees of presuppositions spanning health, science, and general knowledge and evaluate several widely-deployed models. |
| Outcome: | The proposed models achieve higher accuracy but fail to challenge a large fraction of false presuppositions. |