# Can QPP Choose the Right Query Variant? Evaluating Query Variant Selection for RAG Pipelines

arXiv: https://arxiv.org/abs/2604.22661 · HTML: https://mlsanity.com/paper/2604.22661/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- Builds on 23 of its 81 references that are in the map.
- First posted on arXiv 24 Apr 2026.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Builds on (most cited first, 23 of 23)

- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation](https://mlsanity.com/paper/2409.12941/) (2024)
- [Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting](https://mlsanity.com/paper/2310.09716/) (2023)
- [LongRAG: A Dual-Perspective Retrieval-Augmented Generation Paradigm for Long-Context Question Answering](https://mlsanity.com/paper/2410.18050/) (2024)
- [Ragnar"ok: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track](https://mlsanity.com/paper/2406.16828/) (2024)
- [Initial Nugget Evaluation Results for the TREC 2024 RAG Track with the AutoNuggetizer Framework](https://mlsanity.com/paper/2411.09607/) (2024)
- [Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls](https://mlsanity.com/paper/2108.11044/) (2021)
- [Generative Query Reformulation for Effective Adhoc Search](https://mlsanity.com/paper/2308.00415/) (2023)
- [GenQREnsemble: Zero-Shot LLM Ensemble Prompting for Generative Query Reformulation](https://mlsanity.com/paper/2404.03746/) (2024)
- [Query Performance Prediction using Relevance Judgments Generated by Large Language Models](https://mlsanity.com/paper/2404.01012/) (2024)
- [METIS: Fast Quality-Aware RAG Systems with Configuration Adaptation](https://mlsanity.com/paper/2412.10543/) (2024)
- [Query Performance Prediction: From Ad-hoc to Conversational Search](https://mlsanity.com/paper/2305.10923/) (2023)
- [A Comparison of Methods for Evaluating Generative IR](https://mlsanity.com/paper/2404.04044/) (2024)
- [Is Relevance Propagated from Retriever to Generator in RAG?](https://mlsanity.com/paper/2502.15025/) (2025)
- [Unsupervised Question Clarity Prediction Through Retrieved Item Coherency](https://mlsanity.com/paper/2208.04882/) (2022)
- [Adapting Standard Retrieval Benchmarks to Evaluate Generated Answers](https://mlsanity.com/paper/2401.04842/) (2024)
- [An Analysis of Variations in the Effectiveness of Query Performance Prediction](https://mlsanity.com/paper/2202.06306/) (2022)
- [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)
- [QueryGym: A Toolkit for Reproducible LLM-Based Query Reformulation](https://mlsanity.com/paper/2511.15996/) (2025)
- [A New Query Expansion Approach via Agent-Mediated Dialogic Inquiry](https://mlsanity.com/paper/2502.08557/) (2025)
- [Uncovering the Limitations of Query Performance Prediction: Failures, Insights, and Implications for Selective Query Processing](https://mlsanity.com/paper/2504.01101/) (2025)
- [Breaking Flat: A Generalised Query Performance Prediction Evaluation Framework](https://mlsanity.com/paper/2601.17359/) (2026)
- [ReFormeR: Learning and Applying Explicit Query Reformulation Patterns](https://mlsanity.com/paper/2604.01417/) (2026)

## Built on by (most cited first, 1 of 1)

- [One Size Does Not Fit All! Dynamic Retriever and Generator Selection for RAG](https://mlsanity.com/paper/2609.17709/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have made query reformulation ubiquitous in modern retrieval and Retrieval-Augmented Generation (RAG) pipelines, enabling the generation of multiple semantically equivalent query variants. However, executing the full pipeline for every reformulation is computationally expensive, motivating selective execution: can we identify the best query variant before incurring downstream retrieval and generation costs? We investigate Query Performance Prediction (QPP) as a mechanism for variant selection across ad-hoc retrieval and end-to-end RAG. Unlike traditional QPP, which estimates query difficulty across topics, we study intra-topic discrimination - selecting the optimal reformulation among competing variants of the same information need. Through large-scale experiments on TREC-RAG using both sparse and dense retrievers, we evaluate pre- and post-retrieval predictors under correlation- and decision-based metrics. Our results reveal a systematic divergence between retrieval and generation objectives: variants that maximize ranking metrics such as nDCG often fail to produce the best generated answers, exposing a "utility gap" between retrieval relevance and generation fidelity. Nevertheless, QPP can reliably identify variants that improve end-to-end quality over the original query. Notably, lightweight pre-retrieval predictors frequently match or outperform more expensive post-retrieval methods, offering a latency-efficient approach to robust RAG.