QP-OneModel: A Unified Generative LLM for Multi-Task Query Understanding in Xiaohongshu Search
Query Processing (QP) bridges user intent and content supply in large-scale Social Network Service (SNS) search engines. Traditional QP systems rely on pipelines of isolated discriminative models (e.g., BERT), suffering from limited semantic understanding and high maintenance overhead. While Large Language Models (LLMs) offer a potential solution, existing approaches often optimize sub-tasks in isolation, neglecting intrinsic semantic synergy and necessitating independent iterations. Moreover, standard generative methods often lack grounding in SNS scenarios, failing to bridge the gap between open-domain corpora and informal SNS linguistic patterns, while struggling to adhere to rigorous business definitions. We present QP-OneModel, a Unified Generative LLM for Multi-Task Query Understanding in the SNS domain. We reformulate heterogeneous sub-tasks into a unified sequence generation paradigm, adopting a progressive three-stage alignment strategy culminating in multi-reward Reinforcement Learning. Furthermore, QP-OneModel generates intent descriptions as a novel high-fidelity semantic signal, effectively augmenting downstream tasks such as query rewriting and ranking. Offline evaluations show QP-OneModel achieves a 7.35% overall gain over discriminative baselines, with significant F1 boosts in NER (+9.01%) and Term Weighting (+9.31%). It also exhibits superior generalization, surpassing a 32B model by 7.60% accuracy on unseen tasks. Fully deployed at Xiaohongshu, online A/B tests confirm its industrial value, optimizing retrieval relevance (DCG) by 0.21% and lifting user retention by 0.044%.
698d472965c0d15a6d16214b
Jianzhao Huang
698d472965c0d15a6d16214c
Xiaorui Huang
698d472965c0d15a6d16214d
Fei Zhao
claimed_verified
2026-02-12T13:28:27.151Z
65328aa39326d6da5ff19b52
/avatars/5c3de984cd6eba69616bb608796865c5.svg
Fei Zhao
Hiiamein
user
Hiiamein
698d472965c0d15a6d16214e
Yunpeng Liu
698d472965c0d15a6d16214f
Hui Zhang
698d472965c0d15a6d162150
Fangcheng Shi
698d472965c0d15a6d162151
Congfeng Li
698d472965c0d15a6d162152
Zechen Sun
698d472965c0d15a6d162153
Yi Wu
698d472965c0d15a6d162154
Yao Hu
698d472965c0d15a6d162155
Yunhan Bai
698d472965c0d15a6d162156
Shaosheng Cao
QP-OneModel: A Unified Generative LLM for Multi-Task Query Understanding in Xiaohongshu Search
Query Processing (QP) bridges user intent and content supply in large-scale Social Network Service (SNS) search engines. Traditional QP systems rely on pipelines of isolated discriminative models (e.g., BERT), suffering from limited semantic understanding and high maintenance overhead. While Large Language Models (LLMs) offer a potential solution, existing approaches often optimize sub-tasks in isolation, neglecting intrinsic semantic synergy and necessitating independent iterations. Moreover, standard generative methods often lack grounding in SNS scenarios, failing to bridge the gap between open-domain corpora and informal SNS linguistic patterns, while struggling to adhere to rigorous business definitions. We present QP-OneModel, a Unified Generative LLM for Multi-Task Query Understanding in the SNS domain. We reformulate heterogeneous sub-tasks into a unified sequence generation paradigm, adopting a progressive three-stage alignment strategy culminating in multi-reward Reinforcement Learning. Furthermore, QP-OneModel generates intent descriptions as a novel high-fidelity semantic signal, effectively augmenting downstream tasks such as query rewriting and ranking. Offline evaluations show QP-OneModel achieves a 7.35% overall gain over discriminative baselines, with significant F1 boosts in NER (+9.01%) and Term Weighting (+9.31%). It also exhibits superior generalization, surpassing a 32B model by 7.60% accuracy on unseen tasks. Fully deployed at Xiaohongshu, online A/B tests confirm its industrial value, optimizing retrieval relevance (DCG) by 0.21% and lifting user retention by 0.044%.
A unified generative large language model approach for social network search query processing that improves semantic understanding through multi-task learning and reinforcement learning while enhancing downstream task performance.
Large Language Models query processing discriminative models sequence generation progressive three-stage alignment multi-reward Reinforcement Learning intent descriptions query rewriting ranking offline evaluations online A/B tests