# Exploiting Simulated User Feedback for Conversational Search: Ranking, Rewriting, and Beyond

arXiv: https://arxiv.org/abs/2304.13874 · HTML: https://mlsanity.com/paper/2304.13874/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2023.

## Built on by (most cited first, 24 of 24)

- [User Behavior Simulation with Large Language Model based Agents](https://mlsanity.com/paper/2306.02552/) (2023)
- [Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactions](https://mlsanity.com/paper/2312.02913/) (2023)
- [Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting](https://mlsanity.com/paper/2310.09716/) (2023)
- [Evaluating Large Language Models as Generative User Simulators for Conversational Recommendation](https://mlsanity.com/paper/2403.09738/) (2024)
- [Interactive Agents: Simulating Counselor-Client Psychological Counseling via Role-Playing LLM-to-LLM Interactions](https://mlsanity.com/paper/2408.15787/) (2024)
- [User Simulation for Evaluating Information Access Systems](https://mlsanity.com/paper/2306.08550/) (2023)
- [Reliable LLM-based User Simulator for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2402.13374/) (2024)
- [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024)
- [STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agents](https://mlsanity.com/paper/2405.12059/) (2024)
- [ELABORATION: A Comprehensive Benchmark on Human-LLM Competitive Programming](https://mlsanity.com/paper/2505.16667/) (2025)
- [Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generation](https://mlsanity.com/paper/2310.13505/) (2023)
- [Query Understanding in LLM-based Conversational Information Seeking](https://mlsanity.com/paper/2504.06356/) (2025)
- [Broadening Access to Simulations for End-Users via Large Language Models: Challenges and Opportunities](https://mlsanity.com/paper/2409.15290/) (2024)
- [My Words Imply Your Opinion: Reader Agent-based Propagation Enhancement for Personalized Implicit Emotion Analysis](https://mlsanity.com/paper/2412.07367/) (2024)
- [Who's Asking? Simulating Role-Based Questions for Conversational AI Evaluation](https://mlsanity.com/paper/2510.16829/) (2025)
- [Fashion-AlterEval: A Dataset for Improved Evaluation of Conversational Recommendation Systems with Alternative Relevant Items](https://mlsanity.com/paper/2507.18017/) (2025)
- [Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2601.13115/) (2026)
- [StudentSim: Training LLM-based Student Simulators](https://mlsanity.com/paper/2609.01591/) (2026)
- [Can Synthetic Query Rewrites Capture User Intent Better than Humans in Retrieval-Augmented Generation?](https://mlsanity.com/paper/2509.22325/) (2025)
- [Do Images Clarify? A Study on the Effect of Images on Clarifying Questions in Conversational Search](https://mlsanity.com/paper/2602.08700/) (2026)
- [LLM-Driven Multi-Turn Task-Oriented Dialogue Synthesis for Realistic Reasoning](https://mlsanity.com/paper/2602.23610/) (2026)
- [5ting at SemEval-2026 Task 8: Strong End-to-End Multi-Turn RAG via LLM-Based Reranking and Faithfulness Control](https://mlsanity.com/paper/2606.28737/) (2026)
- [On Evaluating and Improving Conversational Agents in Production](https://mlsanity.com/paper/2609.32092/) (2026)
- [RALI@iKAT 2026: Retrieval-aware User Profile Denoising via Personalized Conversational Dense Retrieval](https://mlsanity.com/paper/2610.17673/) (2026)

## Abstract (arXiv)

This research aims to explore various methods for assessing user feedback in mixed-initiative conversational search (CS) systems. While CS systems enjoy profuse advancements across multiple aspects, recent research fails to successfully incorporate feedback from the users. One of the main reasons for that is the lack of system-user conversational interaction data. To this end, we propose a user simulator-based framework for multi-turn interactions with a variety of mixed-initiative CS systems. Specifically, we develop a user simulator, dubbed ConvSim, that, once initialized with an information need description, is capable of providing feedback to a system's responses, as well as answering potential clarifying questions. Our experiments on a wide variety of state-of-the-art passage retrieval and neural re-ranking models show that effective utilization of user feedback can lead to 16% retrieval performance increase in terms of nDCG@3. Moreover, we observe consistent improvements as the number of feedback rounds increases (35% relative improvement in terms of nDCG@3 after three rounds). This points to a research gap in the development of specific feedback processing modules and opens a potential for significant advancements in CS. To support further research in the topic, we release over 30,000 transcripts of system-simulator interactions based on well-established CS datasets.