| Challenge: | Ambiguous questions have different answers depending on their interpretation and can take diverse forms. |
| Approach: | They propose a manually annotated temporally ambiguous QA dataset that captures temporal ambiguity and propose different search strategies based on disambiguate versions of the questions. |
| Outcome: | The proposed approach captures temporal ambiguity and provides non-search, competitive baselines for detecting temporal and few-shot ambiguities. |
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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. |
| Outcome: | The proposed task is based on a dataset covering 14,042 open-domain questions . it shows that strong models benefit from weakly supervised learning . |
Asking Clarification Questions to Handle Ambiguity in Open-Domain QA (2023.findings-emnlp)
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| Challenge: | Ambiguous questions persist in open-domain question answering because formulating a precise question with a unique answer is often challenging. |
| Approach: | They propose to ask a clarification question where the user’s response will help identify the interpretation that best aligns with the user's intention. |
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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. |
| Approach: | They propose a benchmark to identify possible implicit assumptions in QA questions . they use retrieved Wikipedia fragments to identify interpretations for a given query . |
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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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Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction (2021.acl-long)
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Yifan Gao, Henghui Zhu, Patrick Ng, Cicero Nogueira dos Santos, Zhiguo Wang, Feng Nan, Dejiao Zhang, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang
| Challenge: | Open-domain question answering is a task to answer questions using passages with diverse topics. |
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It’s High Time: A Survey of Temporal Question Answering (2026.acl-long)
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| Challenge: | Temporal Question Answering (TQA) is a research area that focuses on answering questions involving temporal constraints or context. |
| Approach: | They present a comprehensive overview of Temporal Question Answering (TQA) this research area focuses on answering questions involving temporal constraints or context . |
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TempTabQA: Temporal Question Answering for Semi-Structured Tables (2023.emnlp-main)
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| Challenge: | Semi-structured data often include temporal information about entities, either implicitly or explicitly. |
| Approach: | They present a dataset that includes 11,454 question-answer pairs from Wikipedia Infobox tables spanning more than 90 distinct domains. |
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ASQA: Factoid Questions Meet Long-Form Answers (2022.emnlp-main)
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| 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. |
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ComplexTempQA: A 100m Dataset for Complex Temporal Question Answering (2025.emnlp-main)
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| Challenge: | Existing datasets that focus on temporal knowledge are limited in size and lack comprehensive coverage of temporal information. |
| Approach: | They introduce a large-scale temporal question-answer-matching dataset . the new taxonomy categorizes questions as attributes, comparisons, and counting questions . |
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Disambiguation in Conversational Question Answering in the Era of LLMs and Agents: A Survey (2025.emnlp-main)
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Mehrab Tanjim, Yeonjun In, Xiang Chen, Victor Bursztyn, Ryan A. Rossi, Sungchul Kim, Guang-Jie Ren, Vaishnavi Muppala, Shun Jiang, Yongsung Kim, Chanyoung Park
| Challenge: | Existing literature on ambiguity and disambiguation with Large Language Models (LLMs) ambiguities are a fundamental challenge in human-AI interactions due to complexity and flexibility of human language. |
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| Outcome: | The proposed frameworks are compared against different disambiguation approaches and highlight their relevance for future research. |