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

arXiv: https://arxiv.org/abs/2602.01023 · HTML: https://mlsanity.com/paper/2602.01023/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Generation and context integration, one of 15 directions in Retrieval-augmented generation.
- Builds on 16 of its 58 references that are in the map.
- First posted on arXiv 1 Feb 2026.

## Placed in

- Retrieval-augmented generation / [Generation and context integration](https://mlsanity.com/rag/generation-and-context-integration/)

## Builds on (most cited first, 16 of 16)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [KTO: Model Alignment as Prospect Theoretic Optimization](https://mlsanity.com/paper/2402.01306/) (2024)
- [A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion](https://mlsanity.com/paper/1507.02221/) (2015)
- [Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion](https://mlsanity.com/paper/2311.06318/) (2023)
- [Personalized Language Model for Query Auto-Completion](https://mlsanity.com/paper/1804.09661/) (2018)
- [On Application of Learning to Rank for E-Commerce Search](https://mlsanity.com/paper/1903.04263/) (2019)
- [Personalized neural language models for real-world query auto completion](https://mlsanity.com/paper/1804.06439/) (2018)
- [Efficient Neural Query Auto Completion](https://mlsanity.com/paper/2008.02879/) (2020)
- [Efficient and Effective Query Auto-Completion](https://mlsanity.com/paper/2005.06213/) (2020)
- [QueryExplorer: An Interactive Query Generation Assistant for Search and Exploration](https://mlsanity.com/paper/2403.15667/) (2024)
- [Personalized Query Auto-Completion for Long and Short-Term Interests with Adaptive Detoxification Generation](https://mlsanity.com/paper/2505.20966/) (2025)
- [High Quality Related Search Query Suggestions using Deep Reinforcement Learning](https://mlsanity.com/paper/2108.04452/) (2021)
- [Deep Pairwise Learning To Rank For Search Autocomplete](https://mlsanity.com/paper/2108.04976/) (2021)

## Abstract (arXiv)

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 methodology that combines RAG, multi-objective DPO, and iterative critique-revision for high-quality synthetic data; (3) 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.