most citedFullStack Bench: Evaluating LLMs as Full Stack Coders

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cs.SE2024

DSTC: Direct Preference Learning with Only Self-Generated Tests and Code to Improve Code LMs

Zhihan Liu, Shenao Zhang, Yongfei Liu +3

Direct preference learning offers a promising and computation-efficient beyond supervised fine-tuning (SFT) for improving code generation in coding large language models (LMs). How…

cs.SE2024★ 1 cited

CodeDPO: Aligning Code Models with Self Generated and Verified Source Code

Kechi Zhang, Ge Li, Yihong Dong +5

Code generation models have shown significant potential for programming tasks. However, existing training methods like supervised fine-tuning face key limitations: they do not effe…