338 citations · 453 across the 14 of their papers we have counts for
5 papers · 1 filter
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…
CELA: Cost-Efficient Language Model Alignment for CTR Prediction
Xingmei Wang, Weiwen Liu, Xiaolong Chen +8
Click-Through Rate (CTR) prediction holds a paramount position in recommender systems. The prevailing ID-based paradigm underperforms in cold-start scenarios due to the skewed dist…
WESE: Weak Exploration to Strong Exploitation for LLM Agents
Xu Huang, Weiwen Liu, Xiaolong Chen +5
Recently, large language models (LLMs) have demonstrated remarkable potential as an intelligent agent. However, existing researches mainly focus on enhancing the agent's reasoning…
RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems
Jianxun Lian, Yuxuan Lei, Xu Huang +3
This paper introduces RecAI, a practical toolkit designed to augment or even revolutionize recommender systems with the advanced capabilities of Large Language Models (LLMs). RecAI…
Understanding the planning of LLM agents: A survey
Xu Huang, Weiwen Liu, Xiaolong Chen +6
As Large Language Models (LLMs) have shown significant intelligence, the progress to leverage LLMs as planning modules of autonomous agents has attracted more attention. This surve…