3 citations · 4 across the 8 of their papers we have counts for
9 papers
OneModel: A Unified Foundation for Platform-Scale Multi-Scenario Ranking
Yinqi Zhang, Peiyu Hu, Yuntian Tang +16
Platform-scale recommender systems often span multiple business streams such as organic recommendation, advertising, and merchant services, where user behaviors form a continuous c…
Code-driven Number Sequence Calculation: Enhancing the inductive Reasoning Abilities of Large Language Models
Kedi Chen, Zhikai Lei, Xu Guo +10
Large language models (LLMs) make remarkable progress in reasoning tasks. Among different reasoning modes, inductive reasoning, due to its better alignment with human learning, att…
A Survey of Inductive Reasoning for Large Language Models
Kedi Chen, Dezhao Ruan, Yuhao Dan +12
Reasoning is an important task for large language models (LLMs). Among all the reasoning paradigms, inductive reasoning is one of the fundamental types, which is characterized by i…
Beyond Quality: Unlocking Diversity in Ad Headline Generation with Large Language Models
Chang Wang, Siyu Yan, Depeng Yuan +8
The generation of ad headlines plays a vital role in modern advertising, where both quality and diversity are essential to engage a broad range of audience segments. Current approa…
Towards Large-scale Generative Ranking
Yanhua Huang, Yuqi Chen, Xiong Cao +17
Generative recommendation has recently emerged as a promising paradigm in information retrieval. However, generative ranking systems are still understudied, particularly with respe…
Complete Chess Games Enable LLM Become A Chess Master
Yinqi Zhang, Xintian Han, Haolong Li +2
Large language models (LLM) have shown remarkable abilities in text generation, question answering, language translation, reasoning and many other tasks. It continues to advance ra…
Exploring Mathematical Extrapolation of Large Language Models with Synthetic Data
Haolong Li, Yu Ma, Yinqi Zhang +2
Large Language Models (LLMs) have shown excellent performance in language understanding, text generation, code synthesis, and many other tasks, while they still struggle in complex…
Progressive Multi-stage Interactive Training in Mobile Network for Fine-grained Recognition
Zhenxin Wu, Qingliang Chen, Yifeng Liu +3
Fine-grained Visual Classification (FGVC) aims to identify objects from subcategories. It is a very challenging task because of the subtle inter-class differences. Existing researc…
Learning Class Unique Features in Fine-Grained Visual Classification
Runkai Zheng, Zhijia Yu, Yinqi Zhang +3
A major challenge in Fine-Grained Visual Classification (FGVC) is distinguishing various categories with high inter-class similarity by learning the feature that differentiate the…