16 citations · 46 across the 10 of their papers we have counts for
5 papers · 1 filter
Executable Code Actions Elicit Better LLM Agents
Xingyao Wang, Yangyi Chen, Lifan Yuan +4
Large Language Model (LLM) agents, capable of performing a broad range of actions, such as invoking tools and controlling robots, show great potential in tackling real-world challe…
R-Tuning: Instructing Large Language Models to Say `I Don't Know'
Hanning Zhang, Shizhe Diao, Yong Lin +6
Large language models (LLMs) have revolutionized numerous domains with their impressive performance but still face their challenges. A predominant issue is the propensity for these…
CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets
Lifan Yuan, Yangyi Chen, Xingyao Wang +3
Large language models (LLMs) are often augmented with tools to solve complex tasks. By generating code snippets and executing them through task-specific Application Programming Int…
MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback
Xingyao Wang, Zihan Wang, Jiateng Liu +4
To solve complex tasks, large language models (LLMs) often require multiple rounds of interactions with the user, sometimes assisted by external tools. However, current evaluation…
Measuring and Improving Chain-of-Thought Reasoning in Vision-Language Models
Yangyi Chen, Karan Sikka, Michael Cogswell +2
Vision-language models (VLMs) have recently demonstrated strong efficacy as visual assistants that can parse natural queries about the visual content and generate human-like output…