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.
Outcome: The proposed approach achieves F1 of 61.3, 25.1, and 40.5 on the three tasks, demonstrating the need for further improvements while providing competitive baselines for future work.

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Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models (2023.emnlp-main)

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Challenge: In open-domain question answering, users often ask ambiguous questions (AQs) . one approach is to identify all possible interpretations of the AQ and generate a long-form answer addressing them all.
Approach: They propose a framework that generates a long-form answer addressing all possible interpretations of an ambiguous question.
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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.
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 .
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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Asking Clarification Questions in Knowledge-Based Question Answering (D19-1)

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Challenge: Existing clarification datasets with limited annotated examples do not address ambiguous phenomena.
Approach: They propose a dataset that allows users to ask clarification questions using open-domain examples.
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ASK: Aspects and Retrieval based Hybrid Clarification in Task Oriented Dialogue Systems (2025.acl-industry)

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Challenge: Ambiguous user queries pose a challenge in task-oriented dialogue systems . Large Language Models (LLMs) rely on the top-k retrieved documents for clarification . traditional approaches lack principled mechanisms to determine when to use broad domain knowledge vs specific retrieved document context for clarification.
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Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction (2021.acl-long)

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Challenge: Open-domain question answering is a task to answer questions using passages with diverse topics.
Approach: They propose a model that aggregates evidence from multiple passages to adaptively predict a single answer or a set of question-answer pairs for ambiguous questions.
Outcome: The proposed model achieves state-of-the-art performance on AmbigQA dataset and shows competitive performance on NQ-Open and TriviaQA.
Teaching Vision-Language Models to Ask: Resolving Ambiguity in Visual Questions (2025.acl-long)

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Challenge: Existing research addresses ambiguous visual questions by rephrasing questions, but it fails to address the inherently interactive nature of user interactions with visual language models (VLMs). Existing studies focus on re-phrase questions, and lack of a benchmark to assess VLMs’ capacity for resolving ambiguities through interaction.
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Answering Ambiguous Questions via Iterative Prompting (2023.acl-long)

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Challenge: Empirical studies show that AmbigPrompt achieves state-of-the-art or competitive results while using less memory and having a lower inference latency than competing approaches.
Approach: They propose an answering model with a prompting model to address imperfections in open-domain question answering . Empirical studies show AmbigPrompt achieves state-of-the-art or competitive results .
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Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions (2021.emnlp-main)

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Challenge: Recent advances on neural approaches to natural language processing have triggered a renaissance in end-to-end neural open-domain chatbots.
Approach: They propose to use offline and online steps to evaluate the quality of clarifying questions in various open-domain dialogues to improve the quality and accuracy of the system response.
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Detecting Temporal Ambiguity in Questions (2024.findings-emnlp)

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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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