2 citations · 6 across the 13 of their papers we have counts for
5 papers · 1 filter
TL-Training: A Task-Feature-Based Framework for Training Large Language Models in Tool Use
Junjie Ye, Yilong Wu, Sixian Li +9
Large language models (LLMs) achieve remarkable advancements by leveraging tools to interact with environments, a critical step toward generalized AI. However, the standard supervi…
SafeAligner: Safety Alignment against Jailbreak Attacks via Response Disparity Guidance
Caishuang Huang, Wanxu Zhao, Rui Zheng +11
As the development of large language models (LLMs) rapidly advances, securing these models effectively without compromising their utility has become a pivotal area of research. How…
ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stages
Junjie Ye, Sixian Li, Guanyu Li +6
Tool learning is widely acknowledged as a foundational approach or deploying large language models (LLMs) in real-world scenarios. While current research primarily emphasizes lever…
RoTBench: A Multi-Level Benchmark for Evaluating the Robustness of Large Language Models in Tool Learning
Junjie Ye, Yilong Wu, Songyang Gao +7
Tool learning has generated widespread interest as a vital means of interaction between Large Language Models (LLMs) and the physical world. Current research predominantly emphasiz…
ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios
Junjie Ye, Guanyu Li, Songyang Gao +9
Existing evaluations of tool learning primarily focus on validating the alignment of selected tools for large language models (LLMs) with expected outcomes. However, these approach…