@inproceedings{chen-etal-2026-localsug,
title = "{L}ocal{SUG}: City-Preference-Enhanced {LLM} for Query Suggestion in Local-Life Services",
author = "Chen, Jinwen and
Zhang, Shiwen and
Gong, Shuai and
Zhang, Zheng and
Zhao, Yachao and
Wang, Lingxiang and
Zhou, Haibo and
Lin, Wei and
Zhang, Hainan",
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.14/",
pages = "211--224",
ISBN = "979-8-89176-460-6",
abstract = "In local-life service platforms, query suggestion reduces user effort by generating candidate queries from input prefixes. Traditional multi-stage systems rely heavily on historical popular queries, limiting their ability to capture long-tail and emerging demand. Although LLMs provide strong semantic generalization, their deployment in local-life services faces three challenges: insufficient city-preference awareness, exposure bias in preference optimization, and strict online latency constraints. We propose LocalSUG, an LLM-based query suggestion framework for local-life services. LocalSUG mines city-preference-enhanced candidates from term co-occurrence and injects them into prompts as dynamic references rather than fusing them into model parameters. This allows the model to adapt to changing city preferences, such as merchant openings or closures, while reducing stale or locally invalid suggestions. We further introduce a beam-search-driven GRPO algorithm to align training with inference-time decoding and optimize relevance together with business-oriented rewards. Finally, quality-aware beam acceleration and vocabulary pruning reduce online latency while preserving generation quality. Offline evaluations and large-scale online A/B testing show that LocalSUG improves CTR by +0.35{\%} and reduces the low/no-result rate by 3.98{\%}, demonstrating its effectiveness in real-world deployment."
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<abstract>In local-life service platforms, query suggestion reduces user effort by generating candidate queries from input prefixes. Traditional multi-stage systems rely heavily on historical popular queries, limiting their ability to capture long-tail and emerging demand. Although LLMs provide strong semantic generalization, their deployment in local-life services faces three challenges: insufficient city-preference awareness, exposure bias in preference optimization, and strict online latency constraints. We propose LocalSUG, an LLM-based query suggestion framework for local-life services. LocalSUG mines city-preference-enhanced candidates from term co-occurrence and injects them into prompts as dynamic references rather than fusing them into model parameters. This allows the model to adapt to changing city preferences, such as merchant openings or closures, while reducing stale or locally invalid suggestions. We further introduce a beam-search-driven GRPO algorithm to align training with inference-time decoding and optimize relevance together with business-oriented rewards. Finally, quality-aware beam acceleration and vocabulary pruning reduce online latency while preserving generation quality. Offline evaluations and large-scale online A/B testing show that LocalSUG improves CTR by +0.35% and reduces the low/no-result rate by 3.98%, demonstrating its effectiveness in real-world deployment.</abstract>
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%0 Conference Proceedings
%T LocalSUG: City-Preference-Enhanced LLM for Query Suggestion in Local-Life Services
%A Chen, Jinwen
%A Zhang, Shiwen
%A Gong, Shuai
%A Zhang, Zheng
%A Zhao, Yachao
%A Wang, Lingxiang
%A Zhou, Haibo
%A Lin, Wei
%A Zhang, Hainan
%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 chen-etal-2026-localsug
%X In local-life service platforms, query suggestion reduces user effort by generating candidate queries from input prefixes. Traditional multi-stage systems rely heavily on historical popular queries, limiting their ability to capture long-tail and emerging demand. Although LLMs provide strong semantic generalization, their deployment in local-life services faces three challenges: insufficient city-preference awareness, exposure bias in preference optimization, and strict online latency constraints. We propose LocalSUG, an LLM-based query suggestion framework for local-life services. LocalSUG mines city-preference-enhanced candidates from term co-occurrence and injects them into prompts as dynamic references rather than fusing them into model parameters. This allows the model to adapt to changing city preferences, such as merchant openings or closures, while reducing stale or locally invalid suggestions. We further introduce a beam-search-driven GRPO algorithm to align training with inference-time decoding and optimize relevance together with business-oriented rewards. Finally, quality-aware beam acceleration and vocabulary pruning reduce online latency while preserving generation quality. Offline evaluations and large-scale online A/B testing show that LocalSUG improves CTR by +0.35% and reduces the low/no-result rate by 3.98%, demonstrating its effectiveness in real-world deployment.
%U https://aclanthology.org/2026.emnlp-industry.14/
%P 211-224
Markdown (Informal)
[LocalSUG: City-Preference-Enhanced LLM for Query Suggestion in Local-Life Services](https://aclanthology.org/2026.emnlp-industry.14/) (Chen et al., EMNLP 2026)
ACL
- Jinwen Chen, Shiwen Zhang, Shuai Gong, Zheng Zhang, Yachao Zhao, Lingxiang Wang, Haibo Zhou, Wei Lin, and Hainan Zhang. 2026. LocalSUG: City-Preference-Enhanced LLM for Query Suggestion in Local-Life Services. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 211–224, Budapest, Hungary. Association for Computational Linguistics.