検索拡張生成と多目的アライメントによるクエリ自動補完におけるランキングと生成の統合

Unifying Ranking and Generation in Query Auto-Completion via Retrieval-Augmented Generation and Multi-Objective Alignment

AuthorsKai Yuan, Anthony Zheng, Jia Hu, Divyanshu Sheth, Hemanth Velaga, Kylee Kim, Matteo Guarrera†, Besim Avci, Xuetao Yin, Jianhua Li, Rajyashree Mukherjee, Sean Suchter

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Query Auto-Completion (QAC) is a critical feature of modern search systems that improves search efficiency by suggesting completions as users type. However, existing approaches face fundamental challenges: traditional retrieve-and-rank pipelines have poor 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:

Reformulating QAC as end-to-end list generation with multi-objective optimization;

A comprehensive methodology combining RAG, multi-objective DPO with learned and rule-based verifiers, and iterative critique-revision for high-quality synthetic data;

A hybrid serving architecture enabling efficient production deployment under strict latency constraints.

Evaluation on a large-scale commercial search platform demonstrates substantial improvements: offline metrics show gains across all dimensions, human evaluation yields +0.40 to +0.69 preference scores, and a controlled online experiment achieves 5.44% reduction in keystrokes and 3.46% increase in suggestion adoption, validating that unified generation with RAG and multi-objective alignment provides an effective solution for production QAC.

This work represents a paradigm shift to end-to-end generation powered by large language models, RAG, and multi-objective alignment, establishing a production-validated framework that can benefit the broader search and recommendation industry.

† University of California, Berkeley

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