@inproceedings{liu-etal-2026-intent,
title = "Intent-Driven Generative Query Suggestion with Query-Level Credit Assignment",
author = "Liu, Xinpeng and
Ma, Lu and
Qiao, Jiayi and
Zhou, Mengyu and
Li, Linglong and
Bian, Xiaofeng and
Chen, Haonan and
Jiang, Xiaoxi and
Jiang, Guanjun",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.92/",
pages = "1558--1569",
ISBN = "979-8-89176-460-6",
abstract = "Generative query suggestion aims to enhance user engagement by anticipating user intents and recommending relevant follow-up queries. A central challenge is to generate slates whose individual queries are useful while the slate covers distinct intents. We propose an Intent-Driven Query Suggestion Framework with dual-stage optimization. First, intent-aware diversity modeling constructs intent-aligned supervised fine-tuning (SFT) data and uses an Intent-Aware Diversity Reward to optimize intent coverage. Second, query-level credit assignment routes individual quality signals to the corresponding query tokens while sharing a slate-level diversity signal across the slate. Experiments on a large-scale production dataset, including online A/B testing and offline evaluation, show improvements in click-through rate, query quality, and intent coverage."
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<abstract>Generative query suggestion aims to enhance user engagement by anticipating user intents and recommending relevant follow-up queries. A central challenge is to generate slates whose individual queries are useful while the slate covers distinct intents. We propose an Intent-Driven Query Suggestion Framework with dual-stage optimization. First, intent-aware diversity modeling constructs intent-aligned supervised fine-tuning (SFT) data and uses an Intent-Aware Diversity Reward to optimize intent coverage. Second, query-level credit assignment routes individual quality signals to the corresponding query tokens while sharing a slate-level diversity signal across the slate. Experiments on a large-scale production dataset, including online A/B testing and offline evaluation, show improvements in click-through rate, query quality, and intent coverage.</abstract>
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%0 Conference Proceedings
%T Intent-Driven Generative Query Suggestion with Query-Level Credit Assignment
%A Liu, Xinpeng
%A Ma, Lu
%A Qiao, Jiayi
%A Zhou, Mengyu
%A Li, Linglong
%A Bian, Xiaofeng
%A Chen, Haonan
%A Jiang, Xiaoxi
%A Jiang, Guanjun
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F liu-etal-2026-intent
%X Generative query suggestion aims to enhance user engagement by anticipating user intents and recommending relevant follow-up queries. A central challenge is to generate slates whose individual queries are useful while the slate covers distinct intents. We propose an Intent-Driven Query Suggestion Framework with dual-stage optimization. First, intent-aware diversity modeling constructs intent-aligned supervised fine-tuning (SFT) data and uses an Intent-Aware Diversity Reward to optimize intent coverage. Second, query-level credit assignment routes individual quality signals to the corresponding query tokens while sharing a slate-level diversity signal across the slate. Experiments on a large-scale production dataset, including online A/B testing and offline evaluation, show improvements in click-through rate, query quality, and intent coverage.
%U https://aclanthology.org/2026.emnlp-industry.92/
%P 1558-1569
Markdown (Informal)
[Intent-Driven Generative Query Suggestion with Query-Level Credit Assignment](https://aclanthology.org/2026.emnlp-industry.92/) (Liu et al., EMNLP 2026)
ACL
- Xinpeng Liu, Lu Ma, Jiayi Qiao, Mengyu Zhou, Linglong Li, Xiaofeng Bian, Haonan Chen, Xiaoxi Jiang, and Guanjun Jiang. 2026. Intent-Driven Generative Query Suggestion with Query-Level Credit Assignment. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1558–1569, Budapest, Hungary. Association for Computational Linguistics.