# Asking Clarification Questions to Handle Ambiguity in Open-Domain QA

arXiv: https://arxiv.org/abs/2305.13808 · HTML: https://mlsanity.com/paper/2305.13808/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 45 references that are in the map.
- First posted on arXiv 23 May 2023.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [AmbigQA: Answering Ambiguous Open-domain Questions](https://mlsanity.com/paper/2004.10645/) (2020)
- [MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers](https://mlsanity.com/paper/2012.15828/) (2020)
- [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://mlsanity.com/paper/2209.11755/) (2022)
- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [Is GPT-3 a Good Data Annotator?](https://mlsanity.com/paper/2212.10450/) (2022)
- [Want To Reduce Labeling Cost? GPT-3 Can Help](https://mlsanity.com/paper/2108.13487/) (2021)
- [USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation](https://mlsanity.com/paper/2005.00456/) (2020)
- [SituatedQA: Incorporating Extra-Linguistic Contexts into QA](https://mlsanity.com/paper/2109.06157/) (2021)
- [Asking Clarifying Questions in Open-Domain Information-Seeking Conversations](https://mlsanity.com/paper/1907.06554/) (2019)
- [Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information](https://mlsanity.com/paper/1805.04655/) (2018)
- [Interpretation of Natural Language Rules in Conversational Machine Reading](https://mlsanity.com/paper/1809.01494/) (2018)
- [ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)](https://mlsanity.com/paper/2009.11352/) (2020)
- [MIMICS: A Large-Scale Data Collection for Search Clarification](https://mlsanity.com/paper/2006.10174/) (2020)
- [INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions](https://mlsanity.com/paper/2207.00746/) (2022)
- [Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models](https://mlsanity.com/paper/2205.00176/) (2022)
- [Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction](https://mlsanity.com/paper/2011.13137/) (2020)

## Built on by (most cited first, 26 of 26)

- [Aligning Language Models to Explicitly Handle Ambiguity](https://mlsanity.com/paper/2404.11972/) (2024)
- [CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models](https://mlsanity.com/paper/2405.12063/) (2024)
- [Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2310.14696/) (2023)
- [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024)
- [AmbigDocs: Reasoning across Documents on Different Entities under the Same Name](https://mlsanity.com/paper/2404.12447/) (2024)
- [Semantic Volume: Quantifying and Detecting both External and Internal Uncertainty in LLMs](https://mlsanity.com/paper/2502.21239/) (2025)
- [Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG Systems](https://mlsanity.com/paper/2407.10670/) (2024)
- [Disambiguate First, Parse Later: Generating Interpretations for Ambiguity Resolution in Semantic Parsing](https://mlsanity.com/paper/2502.18448/) (2025)
- [ClarifyMT-Bench: Benchmarking and Improving Multi-Turn Clarification for Conversational Large Language Models](https://mlsanity.com/paper/2512.21120/) (2025)
- [Disambiguation in Conversational Question Answering in the Era of LLMs and Agents: A Survey](https://mlsanity.com/paper/2505.12543/) (2025)
- [QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMs](https://mlsanity.com/paper/2406.14277/) (2024)
- [Ambiguity Collapse by LLMs: A Taxonomy of Epistemic Risks](https://mlsanity.com/paper/2603.05801/) (2026)
- [Knowing but Not Showing: LLMs Recognize Ambiguity but Rarely Ask Clarifying Questions](https://mlsanity.com/paper/2605.25284/) (2026)
- [Alexpaca: Learning Factual Clarification Question Generation Without Examples](https://mlsanity.com/paper/2310.11571/) (2023)
- [SCICONVBENCH: Benchmarking LLMs on Multi-Turn Clarification for Task Formulation in Computational Science](https://mlsanity.com/paper/2605.18630/) (2026)
- [AQuA: Toward Strategic Response Generation for Ambiguous Visual Questions](https://mlsanity.com/paper/2603.07394/) (2026)
- [Talking to a Know-It-All GPT or a Second-Guesser Claude? How Repair reveals unreliable Multi-Turn Behavior in LLMs](https://mlsanity.com/paper/2604.19245/) (2026)
- [One More Turn, Less Regret: A Regret-Based Multi-Turn Benchmark for LLMs' Clarification Policies](https://mlsanity.com/paper/2607.21143/) (2026)
- [Closing the Expression Gap in LLM Instructions via Socratic Questioning](https://mlsanity.com/paper/2510.27410/) (2025)
- [RAC: Retrieval-Augmented Clarification for Faithful Conversational Search](https://mlsanity.com/paper/2601.11722/) (2026)
- [TIPS: Topological Ill-Posedness Probing and Steering in Large Language Models](https://mlsanity.com/paper/2606.23590/) (2026)
- [Fewer Clarifications, Better Code: Benchmarking Cross-Session Personalized Ambiguity Adaptation in Coding Assistants](https://mlsanity.com/paper/2607.26611/) (2026)
- [A knowledge-guided agentic framework for mitigating patient-context ambiguity in health queries](https://mlsanity.com/paper/2608.19875/) (2026)
- [Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization](https://mlsanity.com/paper/2609.05258/) (2026)
- [AM-Bench: Understanding Agents under Task Underspecification across Models and Harnesses](https://mlsanity.com/paper/2610.18090/) (2026)
- [What Is a Clarification? Estimating Model-Dependent Input Ambiguity](https://mlsanity.com/paper/2610.20052/) (2026)

## Abstract (arXiv)

Ambiguous questions persist in open-domain question answering, because formulating a precise question with a unique answer is often challenging. Previously, Min et al. (2020) have tackled this issue by generating disambiguated questions for all possible interpretations of the ambiguous question. This can be effective, but not ideal for providing an answer to the user. Instead, we propose to ask a clarification question, where the user's response will help identify the interpretation that best aligns with the user's intention. We first present CAMBIGNQ, a dataset consisting of 5,654 ambiguous questions, each with relevant passages, possible answers, and a clarification question. The clarification questions were efficiently created by generating them using InstructGPT and manually revising them as necessary. We then define a pipeline of tasks and design appropriate evaluation metrics. Lastly, we achieve 61.3 F1 on ambiguity detection and 40.5 F1 on clarification-based QA, providing strong baselines for future work.