8 citations · 27 across the 33 of their papers we have counts for
3 papers · 1 filter
Teaching LLMs to Learn Tool Trialing and Execution through Environment Interaction
Xingjie Gao, Pengcheng Huang, Zhenghao Liu +6
Equipping Large Language Models (LLMs) with external tools enables them to solve complex real-world problems. However, the robustness of existing methods remains a critical challen…
COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis
Weiqing Yang, Hanbin Wang, Zhenghao Liu +7
Code debugging is a vital stage of software development, essential for ensuring the reliability and performance of Large Language Models (LLMs) in the code generation task. Human d…
INTERVENOR: Prompting the Coding Ability of Large Language Models with the Interactive Chain of Repair
Hanbin Wang, Zhenghao Liu, Shuo Wang +4
This paper introduces INTERVENOR (INTERactiVE chaiN Of Repair), a system designed to emulate the interactive code repair processes observed in humans, encompassing both code diagno…