Challenge: Visual question answering models seek to answer questions about images . ambiguity can exist at all levels of linguistic analysis, but disagreements can be difficult to detect and resolve .
Approach: They develop a question-generation model which integrates group information without supervision and uses a dataset of ambiguous examples to annotate answers.
Outcome: The proposed model can integrate answer group information without supervision and is able to fill knowledge gaps and convey requests.

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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.
Outcome: The proposed models can generate long-form answers that combine conflicting information and provide valuable insights into the limitations of the current approaches.
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.
Approach: They propose a visual question answering task that provides a natural language answer to a question based on a given image and an automated pipeline to generate ambiguity-clarification question pairs.
Outcome: The proposed benchmark targets three common categories of ambiguity in visual question answering (VQA) context and encompasses various VQA scenarios.
Know What I don’t Know: Handling Ambiguous and Unknown Questions for Text-to-SQL (2023.findings-acl)

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Challenge: Existing text-to-SQL parsers generate a plausible SQL query for arbitrary user questions, thereby failing to handle problematic user questions.
Approach: They propose a weakly supervised DTE model for error detection, localization, and explanation.
Outcome: The proposed model achieves the best result on real-world examples and generated examples compared with baselines.
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.
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.
Disambiguation in Conversational Question Answering in the Era of LLMs and Agents: A Survey (2025.emnlp-main)

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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.
Approach: They propose to define key terms and concepts and categorize various disambiguation approaches enabled by LLMs and provide a comparative analysis of their advantages and disadvantages.
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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.
Looking Beyond the One: Operationalizing and Eliciting Visual Ambiguity in VLLMs (2026.acl-long)

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Challenge: Visual question answering systems typically collapse ambiguity, committing to a single interpretation during decoding and evaluation.
Approach: They operationalize ambiguity as the existence of multiple answer-supporting regions in an image . they show that ambiguities are already encoded in their internal representations .
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The Problem of Ambiguity in Table Question Answering (2026.findings-eacl)

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Challenge: Existing approaches to question answering on tabular data have limited capabilities due to ambiguousness inherent to tabular datasets.
Approach: They propose to use large language models to answer questions on tabular data by analyzing tabular tables and detecting ambiguity.
Outcome: The proposed model can detect ambiguity in tabular data and provide an initial ground for a deeper discussion on how to approach it in the age of LLMs.
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.
Outcome: The proposed framework outperforms baselines on ASQA in a few-shot setup across metrics while surpassing fully-supervised baselines trained on the whole training set in terms of Disambig-F1 and Disambigo-ROUGE.
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 .

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