5 papers
AskToAct: Enhancing LLMs Tool Use via Self-Correcting Clarification
Xuan Zhang, Yongliang Shen, Zhe Zheng +6
Large language models (LLMs) have demonstrated remarkable capabilities in tool learning. In real-world scenarios, user queries are often ambiguous and incomplete, requiring effecti…
LLM-based Multi-Agent Systems: Techniques and Business Perspectives
Yingxuan Yang, Qiuying Peng, Jun Wang +2
In the era of (multi-modal) large language models, most operational processes can be reformulated and reproduced using LLM agents. The LLM agents can perceive, control, and get fee…
Hammer: Robust Function-Calling for On-Device Language Models via Function Masking
Qiqiang Lin, Muning Wen, Qiuying Peng +8
Large language models have demonstrated impressive value in performing as autonomous agents when equipped with external tools and API calls. Nonetheless, effectively harnessing the…
Graph Propagation Transformer for Graph Representation Learning
Zhe Chen, Hao Tan, Tao Wang +5
This paper presents a novel transformer architecture for graph representation learning. The core insight of our method is to fully consider the information propagation among nodes…
Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives
Wenqi Zhang, Yongliang Shen, Linjuan Wu +4
The reflection capacity of Large Language Model (LLM) has garnered extensive attention. A post-hoc prompting strategy, e.g., reflexion and self-refine, refines LLM's response based…