May 8, 2024, 4:47 a.m. | Mayank Mishra, Matt Stallone, Gaoyuan Zhang, Yikang Shen, Aditya Prasad, Adriana Meza Soria, Michele Merler, Parameswaran Selvam, Saptha Surendran, Sh

cs.CL updates on arXiv.org arxiv.org

arXiv:2405.04324v1 Announce Type: cross
Abstract: Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environments to improve the productivity of human programmers, and LLM-based agents are beginning to show promise for handling complex tasks autonomously. Realizing the full potential of code LLMs requires a wide range of capabilities, including code generation, fixing bugs, explaining and documenting code, maintaining repositories, and more. In this work, we introduce the …

abstract agents arxiv code code intelligence code llms cs.ai cs.cl cs.se development environments family foundation human intelligence language language models large language large language models llm llms process productivity programmers show software software development tasks type

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