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About this paper
Nam Huynh, Beiyu Lin
This comprehensive survey consolidates the landscape of Large Language Models for code generation, categorizing current challenges, surveying fine-tuning techniques, detailing evaluation methods, and showcasing diverse applications to guide future research and development. It highlights the potential for democratizing coding while addressing critical limitations.
Key insights
- LLMs for code generation currently face substantial challenges, including high computational resource demands, prevalence of syntactic and semantic errors, various biases (multilingual, multi-programming language, social), and significant security risks leading to vulnerable code.
- Effective enhancement strategies involve fine-tuning models on domain-specific datasets, incorporating iterative feedback from execution or human review, and employing advanced prompt engineering techniques like Chain-of-Thought or intelligent example retrieval.
- Robust evaluation of LLM-generated code requires a combination of advanced metrics (e.g., CodeBLEU, pass@k) and increasingly complex benchmarks (e.g., HumanEval, ClassEval, SWE-bench) that move beyond function-level tasks to real-world software engineering scenarios.
Summary by alphaXiv.
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