# Query Performance Prediction: From Ad-hoc to Conversational Search

arXiv: https://arxiv.org/abs/2305.10923 · HTML: https://mlsanity.com/paper/2305.10923/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 67 references that are in the map.
- First posted on arXiv 18 May 2023.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [Asking Clarifying Questions in Open-Domain Information-Seeking Conversations](https://mlsanity.com/paper/1907.06554/) (2019)
- [Open-Retrieval Conversational Question Answering](https://mlsanity.com/paper/2005.11364/) (2020)
- [Few-Shot Generative Conversational Query Rewriting](https://mlsanity.com/paper/2006.05009/) (2020)
- [Few-Shot Conversational Dense Retrieval](https://mlsanity.com/paper/2105.04166/) (2021)
- [CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning](https://mlsanity.com/paper/2112.08558/) (2021)
- [Query Resolution for Conversational Search with Limited Supervision](https://mlsanity.com/paper/2005.11723/) (2020)
- [Contextualized Query Embeddings for Conversational Search](https://mlsanity.com/paper/2104.08707/) (2021)
- [Conversational Information Seeking](https://mlsanity.com/paper/2201.08808/) (2022)
- [RefNet: A Reference-aware Network for Background Based Conversation](https://mlsanity.com/paper/1908.06449/) (2019)
- [Saving Dense Retriever from Shortcut Dependency in Conversational Search](https://mlsanity.com/paper/2202.07280/) (2022)
- [A Comparison of Question Rewriting Methods for Conversational Passage Retrieval](https://mlsanity.com/paper/2101.07382/) (2021)
- [Deep-QPP: A Pairwise Interaction-based Deep Learning Model for Supervised Query Performance Prediction](https://mlsanity.com/paper/2202.07376/) (2022)
- [Harnessing Evolution of Multi-Turn Conversations for Effective Answer Retrieval](https://mlsanity.com/paper/1912.10554/) (2019)
- [Topic Propagation in Conversational Search](https://mlsanity.com/paper/2004.14054/) (2020)
- [Unsupervised Question Clarity Prediction Through Retrieved Item Coherency](https://mlsanity.com/paper/2208.04882/) (2022)
- [An Analysis of Variations in the Effectiveness of Query Performance Prediction](https://mlsanity.com/paper/2202.06306/) (2022)
- [Groupwise Query Performance Prediction with BERT](https://mlsanity.com/paper/2204.11489/) (2022)

## Built on by (most cited first, 16 of 16)

- [Benchmarking Prompt Sensitivity in Large Language Models](https://mlsanity.com/paper/2502.06065/) (2025)
- [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024)
- [Ranked List Truncation for Large Language Model-based Re-Ranking](https://mlsanity.com/paper/2404.18185/) (2024)
- [Query Performance Prediction using Relevance Judgments Generated by Large Language Models](https://mlsanity.com/paper/2404.01012/) (2024)
- [Can We Use Large Language Models to Fill Relevance Judgment Holes?](https://mlsanity.com/paper/2405.05600/) (2024)
- [Conversational Search: From Fundamentals to Frontiers in the LLM Era](https://mlsanity.com/paper/2506.10635/) (2025)
- [Am I on the Right Track? What Can Predicted Query Performance Tell Us about the Search Behaviour of Agentic RAG](https://mlsanity.com/paper/2507.10411/) (2025)
- [Generative Retrieval with Few-shot Indexing](https://mlsanity.com/paper/2408.02152/) (2024)
- [PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Prediction](https://mlsanity.com/paper/2406.04746/) (2024)
- [Uncovering the Limitations of Query Performance Prediction: Failures, Insights, and Implications for Selective Query Processing](https://mlsanity.com/paper/2504.01101/) (2025)
- [Query Augmentation by Decoding Semantics from Brain Signals](https://mlsanity.com/paper/2402.15708/) (2024)
- [Adaptive Personalized Conversational Information Retrieval](https://mlsanity.com/paper/2508.08634/) (2025)
- [Can QPP Choose the Right Query Variant? Evaluating Query Variant Selection for RAG Pipelines](https://mlsanity.com/paper/2604.22661/) (2026)
- [LLM-Oriented Information Retrieval: A Denoising-First Perspective](https://mlsanity.com/paper/2605.00505/) (2026)
- [The Surprising Effectiveness of Rankers Trained on Expanded Queries](https://mlsanity.com/paper/2404.02587/) (2024)
- [VQPP: Video Query Performance Prediction Benchmark](https://mlsanity.com/paper/2602.17814/) (2026)

## Abstract (arXiv)

Query performance prediction (QPP) is a core task in information retrieval. The QPP task is to predict the retrieval quality of a search system for a query without relevance judgments. Research has shown the effectiveness and usefulness of QPP for ad-hoc search. Recent years have witnessed considerable progress in conversational search (CS). Effective QPP could help a CS system to decide an appropriate action to be taken at the next turn. Despite its potential, QPP for CS has been little studied. We address this research gap by reproducing and studying the effectiveness of existing QPP methods in the context of CS. While the task of passage retrieval remains the same in the two settings, a user query in CS depends on the conversational history, introducing novel QPP challenges. In particular, we seek to explore to what extent findings from QPP methods for ad-hoc search generalize to three CS settings: (i) estimating the retrieval quality of different query rewriting-based retrieval methods, (ii) estimating the retrieval quality of a conversational dense retrieval method, and (iii) estimating the retrieval quality for top ranks vs. deeper-ranked lists. Our findings can be summarized as follows: (i) supervised QPP methods distinctly outperform unsupervised counterparts only when a large-scale training set is available; (ii) point-wise supervised QPP methods outperform their list-wise counterparts in most cases; and (iii) retrieval score-based unsupervised QPP methods show high effectiveness in assessing the conversational dense retrieval method, ConvDR.