5 papers
APAO: Bridging the Training-Inference Gap in Generative Recommendation via Adaptive Prefix-Aware Optimization
Yuanqing Yu, Yifan Wang, Weizhi Ma +2
Generative recommendation has recently emerged as a promising paradigm for sequential recommendation. It formulates the task as an autoregressive generation process, predicting tok…
StepTool: Enhancing Multi-Step Tool Usage in LLMs via Step-Grained Reinforcement Learning
Yuanqing Yu, Zhefan Wang, Weizhi Ma +4
Despite their powerful text generation capabilities, large language models (LLMs) still struggle to effectively utilize external tools to solve complex tasks, a challenge known as…
ToolACE: Winning the Points of LLM Function Calling
Weiwen Liu, Xu Huang, Xingshan Zeng +24
Function calling significantly extends the application boundary of large language models, where high-quality and diverse training data is critical for unlocking this capability. Ho…
AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems
Yu Shang, Peijie Liu, Yuwei Yan +9
The emergence of agentic recommender systems powered by Large Language Models (LLMs) represents a paradigm shift in personalized recommendations, leveraging LLMs' advanced reasonin…
SPRec: Self-Play to Debias LLM-based Recommendation
Chongming Gao, Ruijun Chen, Shuai Yuan +3
Large language models (LLMs) have attracted significant attention in recommendation systems. Current work primarily applies supervised fine-tuning (SFT) to adapt the model for reco…