REVIEW 5 cited by
EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
As large language models (LLMs) play an increasingly important role in code generation, enhancing both correctness and efficiency has become crucial. Current methods primarily focus on correctness, often overlooking efficiency. To address this gap, we introduce EffiCoder to improve both aspects by fine-tuning LLMs on a high-quality dataset comprising correct and efficient code samples. Our methodology involves leveraging multiple LLMs to generate diverse candidate code solutions for various tasks across different programming languages. We then evaluate these solutions by measuring their execution time and memory usage through local execution. The code solution with the lowest execution time and memory consumption is selected as the final output for each task. Experimental results demonstrate significant improvements when fine-tuning with Effi-Instruct. For instance, Qwen2.5-Coder-7B-Instruct's pass@1 score increases from 44.8\% to 57.7\%, while the average execution time for correct tasks decreases by 48.4\%. EffiCoder offers a scalable and effective solution for advancing AI-driven code generation, benefiting software development and computational problem-solving. The source code of Effi-Code was released at https://github.com/huangd1999/EffiCoder.
Forward citations
Cited by 5 Pith papers
-
Multi-Source and Cross-Scenario Strategy-Guided Code Optimization
MoST improves LLM-guided code optimization by clustering optimization strategies from heterogeneous knowledge sources and transferring them across programming languages.
-
SemOpt: LLM-Driven Code Optimization via Rule-Based Analysis
SemOpt generates Semgrep static-analysis rules from LLM-summarized optimization commits and uses them to locate and apply optimization strategies, outperforming retrieval-based baselines on C/C++ code.
-
Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization
Reinforcement learning with execution feedback enables a code model to iteratively improve the efficiency of its own generated code, surpassing supervised and preference-based training methods.
-
Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming
On 150 LeetCode problems, GPT-4.0 and DeepSeek-Reasoner beat three 3B-parameter models on correctness and speed; the 52% energy-efficiency claim counts any of three SLMs on correct outputs, not a per-model advantage.
-
Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead
A literature review organizes LLM development into a six-phase software engineering lifecycle and identifies challenges and research directions for each phase.
Discussion (0). Continue with ORCID to comment.