RoMQA: A Benchmark for Robust, Multi-evidence, Multi-answer Question Answering (2023.findings-emnlp)
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| Challenge: | Existing QA models are not robust to variations in question constraints, but can be made more robust by tuning on clusters of related questions. |
| Approach: | They introduce RoMQA, the first benchmark for robust, multi-evidence, multianswer question answering (QA) RoMQ contains clusters of related questions that are derived from the Wikidata knowledge graph . |
| Outcome: | The proposed model is the first benchmark for robust, multi-evidence, multianswer question answering (QA) compared to prior QA datasets, it has more human-written questions that require reasoning over more evidence text and have, on average, many more correct answers. |
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