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cs.CL2025
EffiBench-X: A Multi-Language Benchmark for Measuring Efficiency of LLM-Generated Code
Yuhao Qing, Boyu Zhu, Mingzhe Du +9
Existing code generation benchmarks primarily evaluate functional correctness, with limited focus on code efficiency and often restricted to a single language like Python. To addre…
cs.CL2024★ 1 cited
EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning
Dong Huang, Guangtao Zeng, Jianbo Dai +6
As large language models (LLMs) play an increasingly important role in code generation, enhancing both correctness and efficiency has become crucial. Current methods primarily focu…
cs.CL2023
AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation
Dong Huang, Jie M. Zhang, Michael Luck +3
The advancement of natural language processing (NLP) has been significantly boosted by the development of transformer-based large language models (LLMs). These models have revoluti…