# STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agents

arXiv: https://arxiv.org/abs/2405.12059 · HTML: https://mlsanity.com/paper/2405.12059/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 22 of its 33 references that are in the map.
- First posted on arXiv 20 May 2024.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents](https://mlsanity.com/paper/2304.09542/) (2023)
- [Recommender Systems in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2307.02046/) (2023)
- [Asking Clarifying Questions in Open-Domain Information-Seeking Conversations](https://mlsanity.com/paper/1907.06554/) (2019)
- [Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration](https://mlsanity.com/paper/2305.13626/) (2023)
- [Clarify When Necessary: Resolving Ambiguity Through Interaction with LMs](https://mlsanity.com/paper/2311.09469/) (2023)
- [Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents](https://mlsanity.com/paper/2311.00262/) (2023)
- [Interactive Path Reasoning on Graph for Conversational Recommendation](https://mlsanity.com/paper/2007.00194/) (2020)
- [CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models](https://mlsanity.com/paper/2212.07769/) (2022)
- [Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions](https://mlsanity.com/paper/2109.05794/) (2021)
- [ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)](https://mlsanity.com/paper/2009.11352/) (2020)
- [PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance](https://mlsanity.com/paper/2210.08817/) (2022)
- [Neural Approaches to Conversational Information Retrieval](https://mlsanity.com/paper/2201.05176/) (2022)
- [Analyzing and Characterizing User Intent in Information-seeking Conversations](https://mlsanity.com/paper/1804.08759/) (2018)
- [A Survey on Asking Clarification Questions Datasets in Conversational Systems](https://mlsanity.com/paper/2305.15933/) (2023)
- [Evaluating Mixed-initiative Conversational Search Systems via User Simulation](https://mlsanity.com/paper/2204.08046/) (2022)
- [Exploiting Simulated User Feedback for Conversational Search: Ranking, Rewriting, and Beyond](https://mlsanity.com/paper/2304.13874/) (2023)
- [Controlling the Risk of Conversational Search via Reinforcement Learning](https://mlsanity.com/paper/2101.06327/) (2021)
- [Domain Adaptation for Enterprise Email Search](https://mlsanity.com/paper/1906.07897/) (2019)
- [Simulating and Modeling the Risk of Conversational Search](https://mlsanity.com/paper/2201.00235/) (2022)

## Built on by (most cited first, 17 of 17)

- [Ask-before-Plan: Proactive Language Agents for Real-World Planning](https://mlsanity.com/paper/2406.12639/) (2024)
- [Knowledge Boundary of Large Language Models: A Survey](https://mlsanity.com/paper/2412.12472/) (2024)
- [ELABORATION: A Comprehensive Benchmark on Human-LLM Competitive Programming](https://mlsanity.com/paper/2505.16667/) (2025)
- [Beyond Persuasion: Towards Conversational Recommender System with Credible Explanations](https://mlsanity.com/paper/2409.14399/) (2024)
- [Query Understanding in LLM-based Conversational Information Seeking](https://mlsanity.com/paper/2504.06356/) (2025)
- [Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives](https://mlsanity.com/paper/2506.09656/) (2025)
- [Navigating the Unknown: A Chat-Based Collaborative Interface for Personalized Exploratory Tasks](https://mlsanity.com/paper/2410.24032/) (2024)
- [How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond](https://mlsanity.com/paper/2501.05714/) (2025)
- [PROPER Agents: Proactivity Driven Personalized Agents for Advancing Knowledge Gap Navigation](https://mlsanity.com/paper/2601.09926/) (2026)
- [METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues](https://mlsanity.com/paper/2604.11427/) (2026)
- [HotelQuEST: Balancing Quality and Efficiency in Agentic Search](https://mlsanity.com/paper/2602.23949/) (2026)
- [Towards Proactive Information Probing: Customer Service Chatbots Harvesting Value from Conversation](https://mlsanity.com/paper/2604.11077/) (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)
- [Clarify Before Executing: A Self-Evolving Agent for Resolving Intent Asymmetry in 3D Tool Orchestration](https://mlsanity.com/paper/2607.16352/) (2026)
- [One More Turn, Less Regret: A Regret-Based Multi-Turn Benchmark for LLMs' Clarification Policies](https://mlsanity.com/paper/2607.21143/) (2026)
- [A knowledge-guided agentic framework for mitigating patient-context ambiguity in health queries](https://mlsanity.com/paper/2608.19875/) (2026)
- [Clarify User Expertise: Towards Proactive Conversational Agents Tailoring Responses to User Proficiency](https://mlsanity.com/paper/2608.22266/) (2026)

## Abstract (arXiv)

Equipping a conversational search engine with strategies regarding when to ask clarification questions is becoming increasingly important across various domains. Attributing to the context understanding capability of LLMs and their access to domain-specific sources of knowledge, LLM-based clarification strategies feature rapid transfer to various domains in a post-hoc manner. However, they still struggle to deliver promising performance on unseen domains, struggling to achieve effective domain transferability. We take the first step to investigate this issue and existing methods tend to produce one-size-fits-all strategies across diverse domains, limiting their search effectiveness. In response, we introduce a novel method, called Style, to achieve effective domain transferability. Our experimental results indicate that Style bears strong domain transferability, resulting in an average search performance improvement of ~10% on four unseen domains.