| Challenge: | Recent progress on factoid question answering (QA) does not easily transfer to the task of long-form QA where the goal is to generate detailed explanations. |
| Approach: | They propose a task that focuses on ambiguous factoid questions which have different correct answers depending on interpretation. |
| Outcome: | The proposed metric is reliable and demonstrates agreement between this metric and human judgments, and reveals a considerable gap between human performance and strong baselines. |
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| Challenge: | Long-form question answering systems provide rich information by presenting paragraph-level answers, but not all information is required to answer the question. |
| Approach: | They propose an extract-and-decontextualize approach to summarize long-form answers using state-of-the-art models. |
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AmbigQA: Answering Ambiguous Open-domain Questions (2020.emnlp-main)
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| Challenge: | Existing open-domain question answering systems assume questions have a single welldefined answer. |
| Approach: | They propose an open-domain question answering task which involves finding every plausible answer and rewriting the question for each one to resolve the ambiguity. |
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CondAmbigQA: A Benchmark and Dataset for Conditional Ambiguous Question Answering (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) generate unreliable responses due to their cognitive alignment of context and intent. |
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ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters (N19-1)
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| Challenge: | ComQA dataset captures question phenomena and the diverse ways in which they are formulated. |
| Approach: | They propose a large dataset of real user questions that captures question phenomena and the diverse ways in which they are formulated. |
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Model Analysis & Evaluation for Ambiguous Question Answering (2023.findings-acl)
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| Challenge: | Ambiguous questions are a challenge for Question Answering models as they require answers that cover multiple interpretations of the original query. |
| Approach: | They aim to investigate whether model/data scaling improves the answers’ quality and whether automated metrics align with human judgment. |
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WikiHowQA: A Comprehensive Benchmark for Multi-Document Non-Factoid Question Answering (2023.acl-long)
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| Challenge: | Answering non-factoid questions (NFQs) is a challenging task, requiring passage-level answers that are difficult to construct and evaluate. |
| Approach: | They propose a multi-document NFQA benchmark built on WikiHow, a website dedicated to answering “how-to” questions. |
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MinosEval: Distinguishing Factoid and Non-Factoid for Tailored Open-Ended QA Evaluation with LLMs (2025.findings-acl)
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| Challenge: | Existing evaluation methods overlook the distinction between factoid and non-factoidic questions. |
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IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions (2023.emnlp-main)
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| Challenge: | Existing open-domain QA tasks focus on questions whose answer can be deduced directly from global factual knowledge. |
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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. |
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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. |
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