| Challenge: | Prior work has focused on the case in which the question is clear and the answer is unambiguous but possibly unknown. |
| Approach: | They propose to use a sampled set of questions to calibrate answers to ambiguous questions with varying model scales. |
| Outcome: | The results show that sampling-based confidence scores help calibrate answers to relatively unambiguous questions, with more dramatic improvements on ambiguous ones. |
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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. |
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. |
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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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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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 . |
Re-Examining Calibration: The Case of Question Answering (2022.findings-emnlp)
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| Challenge: | Existing calibration methods do not provide significant gains in accuracy. |
| Approach: | They propose a new calibration metric that better captures whether the model assigns low confidence to wrong predictions and high confidence to correct predictions. |
| Outcome: | The proposed calibration method better captures whether the model assigns low confidence to wrong predictions and high confidence to correct predictions. |
Knowing More About Questions Can Help: Improving Calibration in Question Answering (2021.findings-acl)
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| Challenge: | Existing work on calibration focuses on model confidence, such as the max probability of the predicted class. |
| Approach: | They propose a calibration method which estimates whether model correctly predicts answer for each question. |
| Outcome: | The proposed calibration method achieves 5-10% gains on reading comprehension benchmarks. |
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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Aligning Language Models to Explicitly Handle Ambiguity (2024.emnlp-main)
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Hyuhng Joon Kim, Youna Kim, Cheonbok Park, Junyeob Kim, Choonghyun Park, Kang Min Yoo, Sang-goo Lee, Taeuk Kim
| Challenge: | Large language models (LLMs) are not specifically trained to deal with ambiguous utterances . ambiguity can lead to varying interpretations of the same input based on different assumptions or background knowledge . |
| Approach: | They propose a pipeline that aligns large language models to manage ambiguous queries . they propose to use their own assessment of perceived ambiguity to detect and manage queries a . |
| Outcome: | Experimental results show that APA empowers LLMs to detect and manage ambiguous queries while retaining the ability to answer clear questions. |
How Ambiguous Are the Rationales for Natural Language Reasoning? A Simple Approach to Handling Rationale Uncertainty (2025.coling-main)
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| Challenge: | Language models have made significant progress on complex reasoning tasks, but it is impossible to obtain perfect rationales from models or even from humans. |
| Approach: | They propose a way to guide models to choose between two different reasoning paths depending on the ambiguity of rationales. |
| Outcome: | The proposed approach leads to robust performance in adversarial scenarios where rationale quality is inconsistent. |