3 citations · 4 across the 10 of their papers we have counts for
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cs.LG2024★ 3 cited
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…
cs.AI2024
Planning with Multi-Constraints via Collaborative Language Agents
Cong Zhang, Derrick Goh Xin Deik, Dexun Li +2
The rapid advancement of neural language models has sparked a new surge of intelligent agent research. Unlike traditional agents, large language model-based agents (LLM agents) hav…
cs.AI2024★ 1 cited
Aligning Crowd Feedback via Distributional Preference Reward Modeling
Dexun Li, Cong Zhang, Kuicai Dong +3
Deep Reinforcement Learning is widely used for aligning Large Language Models (LLM) with human preference. However, the conventional reward modelling is predominantly dependent on…