March 29, 2024, 4:42 a.m. | Chunqiu Steven Xia, Yinlin Deng, Lingming Zhang

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.19114v1 Announce Type: cross
Abstract: LLMs have become the go-to choice for code generation tasks, with an exponential increase in the training, development, and usage of LLMs specifically for code generation. To evaluate the ability of LLMs on code, both academic and industry practitioners rely on popular handcrafted benchmarks. However, prior benchmarks contain only a very limited set of problems, both in quantity and variety. Further, due to popularity and age, many benchmarks are prone to data leakage where example …

arxiv benchmarks coding cs.cl cs.lg cs.pl cs.se leaderboard llm ranking type via

Data Engineer

@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania

Artificial Intelligence – Bioinformatic Expert

@ University of Texas Medical Branch | Galveston, TX

Lead Developer (AI)

@ Cere Network | San Francisco, US

Research Engineer

@ Allora Labs | Remote

Ecosystem Manager

@ Allora Labs | Remote

Founding AI Engineer, Agents

@ Occam AI | New York