# MIMICS: A Large-Scale Data Collection for Search Clarification

arXiv: https://arxiv.org/abs/2006.10174 · HTML: https://mlsanity.com/paper/2006.10174/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

## Built on by (most cited first, 27 of 27)

- [Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions](https://mlsanity.com/paper/2109.05794/) (2021)
- [PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance](https://mlsanity.com/paper/2210.08817/) (2022)
- [Asking Clarification Questions to Handle Ambiguity in Open-Domain QA](https://mlsanity.com/paper/2305.13808/) (2023)
- [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024)
- [Post-Abstention: Towards Reliably Re-Attempting the Abstained Instances in QA](https://mlsanity.com/paper/2305.01812/) (2023)
- [INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning](https://mlsanity.com/paper/2401.06532/) (2024)
- [Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction](https://mlsanity.com/paper/2301.10309/) (2023)
- [Alexa, Let's Work Together: Introducing the First Alexa Prize TaskBot Challenge on Conversational Task Assistance](https://mlsanity.com/paper/2209.06321/) (2022)
- [Analysing Mixed Initiatives and Search Strategies during Conversational Search](https://mlsanity.com/paper/2109.05955/) (2021)
- [Database Search Results Disambiguation for Task-Oriented Dialog Systems](https://mlsanity.com/paper/2112.08351/) (2021)
- [Asking Multimodal Clarifying Questions in Mixed-Initiative Conversational Search](https://mlsanity.com/paper/2402.07742/) (2024)
- [Conversational Tree Search: A New Hybrid Dialog Task](https://mlsanity.com/paper/2303.10227/) (2023)
- [Learning to Ask: Conversational Product Search via Representation Learning](https://mlsanity.com/paper/2411.14466/) (2024)
- [Deciding Whether to Ask Clarifying Questions in Large-Scale Spoken Language Understanding](https://mlsanity.com/paper/2109.12451/) (2021)
- [Zero-Shot and Efficient Clarification Need Prediction in Conversational Search](https://mlsanity.com/paper/2503.00179/) (2025)
- [A Transformer-Based User Satisfaction Prediction for Proactive Interaction Mechanism in DuerOS](https://mlsanity.com/paper/2212.03817/) (2022)
- [Enhanced Facet Generation with LLM Editing](https://mlsanity.com/paper/2403.16345/) (2024)
- [Corpus-informed Retrieval Augmented Generation of Clarifying Questions](https://mlsanity.com/paper/2409.18575/) (2024)
- [Detecting Temporal Ambiguity in Questions](https://mlsanity.com/paper/2409.17046/) (2024)
- [On Reference (In-)Determinacy in Natural Language Inference](https://mlsanity.com/paper/2502.05793/) (2025)
- [PSCon: Product Search Through Conversations](https://mlsanity.com/paper/2502.13881/) (2025)
- [Explainability of Text Processing and Retrieval Methods: A Survey](https://mlsanity.com/paper/2212.07126/) (2022)
- [Reward-Driven Interaction: Enhancing Proactive Dialogue Agents through User Satisfaction Prediction](https://mlsanity.com/paper/2505.18731/) (2025)
- [DETOUR: An Interactive Benchmark for Dual-Agent Search and Reasoning](https://mlsanity.com/paper/2602.00352/) (2026)
- [Do Images Clarify? A Study on the Effect of Images on Clarifying Questions in Conversational Search](https://mlsanity.com/paper/2602.08700/) (2026)
- [Clarification Is Not Enough: Post-Clarification Answering Remains the Bottleneck in Multi-Turn QA](https://mlsanity.com/paper/2605.25204/) (2026)
- [Clarify-Then-Search: A Clarification Benchmark for Deep Search with End-to-End Nugget Restoration](https://mlsanity.com/paper/2608.20357/) (2026)

## Abstract (arXiv)

Search clarification has recently attracted much attention due to its applications in search engines. It has also been recognized as a major component in conversational information seeking systems. Despite its importance, the research community still feels the lack of a large-scale data for studying different aspects of search clarification. In this paper, we introduce MIMICS, a collection of search clarification datasets for real web search queries sampled from the Bing query logs. Each clarification in MIMICS is generated by a Bing production algorithm and consists of a clarifying question and up to five candidate answers. MIMICS contains three datasets: (1) MIMICS-Click includes over 400k unique queries, their associated clarification panes, and the corresponding aggregated user interaction signals (i.e., clicks). (2) MIMICS-ClickExplore is an exploration data that includes aggregated user interaction signals for over 60k unique queries, each with multiple clarification panes. (3) MIMICS-Manual includes over 2k unique real search queries. Each query-clarification pair in this dataset has been manually labeled by at least three trained annotators. It contains graded quality labels for the clarifying question, the candidate answer set, and the landing result page for each candidate answer. MIMICS is publicly available for research purposes, thus enables researchers to study a number of tasks related to search clarification, including clarification generation and selection, user engagement prediction for clarification, click models for clarification, and analyzing user interactions with search clarification.