# Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search

arXiv: https://arxiv.org/abs/2303.06573 · HTML: https://mlsanity.com/paper/2303.06573/

## Key facts

- Cited by 75 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2023.

## Built on by (most cited first, 30 of 75)

- [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024)
- [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023)
- [Video Understanding with Large Language Models: A Survey](https://mlsanity.com/paper/2312.17432/) (2023)
- [ConvGQR: Generative Query Reformulation for Conversational Search](https://mlsanity.com/paper/2305.15645/) (2023)
- [Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrieval](https://mlsanity.com/paper/2408.01875/) (2024)
- [RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit](https://mlsanity.com/paper/2306.05212/) (2023)
- [Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learning](https://mlsanity.com/paper/2503.24289/) (2025)
- [Grounding Language Model with Chunking-Free In-Context Retrieval](https://mlsanity.com/paper/2402.09760/) (2024)
- [CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search](https://mlsanity.com/paper/2406.05013/) (2024)
- [VideoINSTA: Zero-shot Long Video Understanding via Informative Spatial-Temporal Reasoning with LLMs](https://mlsanity.com/paper/2409.20365/) (2024)
- [Learning to Relate to Previous Turns in Conversational Search](https://mlsanity.com/paper/2306.02553/) (2023)
- [MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion](https://mlsanity.com/paper/2310.19056/) (2023)
- [Ask Optimal Questions: Aligning Large Language Models with Retriever's Preference in Conversation](https://mlsanity.com/paper/2402.11827/) (2024)
- [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024)
- [INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning](https://mlsanity.com/paper/2401.06532/) (2024)
- [ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval](https://mlsanity.com/paper/2404.13556/) (2024)
- [Enhancing Interactive Image Retrieval With Query Rewriting Using Large Language Models and Vision Language Models](https://mlsanity.com/paper/2404.18746/) (2024)
- [GAR-meets-RAG Paradigm for Zero-Shot Information Retrieval](https://mlsanity.com/paper/2310.20158/) (2023)
- [Generalizing Conversational Dense Retrieval via LLM-Cognition Data Augmentation](https://mlsanity.com/paper/2402.07092/) (2024)
- [Thought-Augmented Planning for LLM-Powered Interactive Recommender Agent](https://mlsanity.com/paper/2506.23485/) (2025)
- [Learning Interpretable Legal Case Retrieval via Knowledge-Guided Case Reformulation](https://mlsanity.com/paper/2406.19760/) (2024)
- [AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignment](https://mlsanity.com/paper/2407.01965/) (2024)
- [UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations](https://mlsanity.com/paper/2507.07030/) (2025)
- [Synergistic Interplay between Search and Large Language Models for Information Retrieval](https://mlsanity.com/paper/2305.07402/) (2023)
- [How to Leverage Personal Textual Knowledge for Personalized Conversational Information Retrieval](https://mlsanity.com/paper/2407.16192/) (2024)
- [Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Search](https://mlsanity.com/paper/2407.20189/) (2024)
- [AMPO: Automatic Multi-Branched Prompt Optimization](https://mlsanity.com/paper/2410.08696/) (2024)
- [Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA](https://mlsanity.com/paper/2409.15515/) (2024)
- [DiSCo: LLM Knowledge Distillation for Efficient Sparse Retrieval in Conversational Search](https://mlsanity.com/paper/2410.14609/) (2024)
- [ConvSDG: Session Data Generation for Conversational Search](https://mlsanity.com/paper/2403.11335/) (2024)

## Abstract (arXiv)

Precisely understanding users' contextual search intent has been an important challenge for conversational search. As conversational search sessions are much more diverse and long-tailed, existing methods trained on limited data still show unsatisfactory effectiveness and robustness to handle real conversational search scenarios. Recently, large language models (LLMs) have demonstrated amazing capabilities for text generation and conversation understanding. In this work, we present a simple yet effective prompting framework, called LLM4CS, to leverage LLMs as a text-based search intent interpreter to help conversational search. Under this framework, we explore three prompting methods to generate multiple query rewrites and hypothetical responses, and propose to aggregate them into an integrated representation that can robustly represent the user's real contextual search intent. Extensive automatic evaluations and human evaluations on three widely used conversational search benchmarks, including CAsT-19, CAsT-20, and CAsT-21, demonstrate the remarkable performance of our simple LLM4CS framework compared with existing methods and even using human rewrites. Our findings provide important evidence to better understand and leverage LLMs for conversational search.