5 papers
MTGenRec: An Efficient Distributed Training System for Generative Recommendation Models in Meituan
Yuxiang Wang, Chi Ma, Xiao Yan +15
Recommendation is crucial for both user experience and company revenue in Meituan as a leading lifestyle company, and generative recommendation models (GRMs) are shown to produce q…
MTServe: Efficient Serving for Generative Recommendation Models with Hierarchical Caches
Xin Wang, Chi Ma, Shaobin Chen +14
Generative recommendation (GR) offers superior modeling capabilities but suffers from prohibitive inference costs due to the repeated encoding of long user histories. While cross-r…
CSSBench: Evaluating the Safety of Lightweight LLMs against Chinese-Specific Adversarial Patterns
Zhenhong Zhou, Shilinlu Yan, Chuanpu Liu +3
Large language models (LLMs) are increasingly deployed in cost-sensitive and on-device scenarios, and safety guardrails have advanced mainly in English. However, real-world Chinese…
Scaling Laws for Black box Adversarial Attacks
Chuan Liu, Huanran Chen, Yichi Zhang +2
Adversarial examples exhibit cross-model transferability, enabling threatening black-box attacks on commercial models. Model ensembling, which attacks multiple surrogate models, is…
MTGR: Industrial-Scale Generative Recommendation Framework in Meituan
Ruidong Han, Bin Yin, Shangyu Chen +12
Scaling law has been extensively validated in many domains such as natural language processing and computer vision. In the recommendation system, recent work has adopted generative…