18 citations · 36 across the 17 of their papers we have counts for
4 papers · 1 filter
RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation
Sichun Luo, Bowei He, Haohan Zhao +9
Large Language Models (LLMs) have demonstrated remarkable capabilities and have been extensively deployed across various domains, including recommender systems. Prior research has…
MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning
Ke Wang, Houxing Ren, Aojun Zhou +7
The recently released GPT-4 Code Interpreter has demonstrated remarkable proficiency in solving challenging math problems, primarily attributed to its ability to seamlessly reason…
Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification
Aojun Zhou, Ke Wang, Zimu Lu +8
Recent progress in large language models (LLMs) like GPT-4 and PaLM-2 has brought significant advancements in addressing math reasoning problems. In particular, OpenAI's latest ver…
PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training
Sichun Luo, Yuanzhang Xiao, Xinyi Zhang +3
Federated recommendation systems employ federated learning techniques to safeguard user privacy by transmitting model parameters instead of raw user data between user devices and t…