![[2602.01023] Unifying Ranking and Generation in Query Auto-Completion via Retrieval-Augmented Generation and Multi-Objective Alignment](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.01023] Unifying Ranking and Generation in Query Auto-Completion via Retrieval-Augmented Generation and Multi-Objective Alignment
Summary
This paper presents a unified framework for Query Auto-Completion (QAC) that integrates Retrieval-Augmented Generation (RAG) and multi-objective optimization to enhance query suggestion efficiency and accuracy.
Why It Matters
The study addresses significant challenges in QAC, such as limited long-tail coverage and the risks of generative methods. By proposing an end-to-end generation framework, it offers a solution that can improve user experience in search and recommendation systems, which is crucial for businesses relying on effective query handling.
Key Takeaways
- Introduces a novel end-to-end framework for QAC using RAG and multi-objective optimization.
- Demonstrates substantial improvements in user interaction metrics, including reduced keystrokes and increased suggestion adoption.
- Combines rule-based, model-based, and LLM-as-judge verifiers for enhanced query completion quality.
Computer Science > Information Retrieval arXiv:2602.01023 (cs) [Submitted on 1 Feb 2026 (v1), last revised 14 Feb 2026 (this version, v4)] Title:Unifying Ranking and Generation in Query Auto-Completion via Retrieval-Augmented Generation and Multi-Objective Alignment Authors:Kai Yuan, Anthony Zheng, Jia Hu, Divyanshu Sheth, Hemanth Velaga, Kylee Kim, Matteo Guarrera, Besim Avci, Jianhua Li, Xuetao Yin, Rajyashree Mukherjee, Sean Suchter View a PDF of the paper titled Unifying Ranking and Generation in Query Auto-Completion via Retrieval-Augmented Generation and Multi-Objective Alignment, by Kai Yuan and 11 other authors View PDF HTML (experimental) Abstract:Query Auto-Completion (QAC) suggests query completions as users type, helping them articulate intent and reach results more efficiently. Existing approaches face fundamental challenges: traditional retrieve-and-rank pipelines have limited long-tail coverage and require extensive feature engineering, while recent generative methods suffer from hallucination and safety risks. We present a unified framework that reformulates QAC as end-to-end list generation through Retrieval-Augmented Generation (RAG) and multi-objective Direct Preference Optimization (DPO). Our approach combines three key innovations: (1) reformulating QAC as end-to-end list generation with multi-objective optimization; (2) defining and deploying a suite of rule-based, model-based, and LLM-as-judge verifiers for QAC, and using them in a comprehensive meth...
