Guided Code Generation with LLMs: A Multi-Agent Framework for Complex Code Tasks
This addresses the problem of enhancing LLM utility in software development for developers, though it is incremental as it builds on existing agent-based methods.
The paper tackles the limitations of LLMs in handling complex, long-context code generation tasks by introducing a multi-agent framework for guided code generation, resulting in a 23.79% improvement in solution accuracy on the HumanEval benchmark compared to direct one-shot generation.
Large Language Models (LLMs) have shown remarkable capabilities in code generation tasks, yet they face significant limitations in handling complex, long-context programming challenges and demonstrating complex compositional reasoning abilities. This paper introduces a novel agentic framework for ``guided code generation'' that tries to address these limitations through a deliberately structured, fine-grained approach to code generation tasks. Our framework leverages LLMs' strengths as fuzzy searchers and approximate information retrievers while mitigating their weaknesses in long sequential reasoning and long-context understanding. Empirical evaluation using OpenAI's HumanEval benchmark with Meta's Llama 3.1 8B model (int4 precision) demonstrates a 23.79\% improvement in solution accuracy compared to direct one-shot generation. Our results indicate that structured, guided approaches to code generation can significantly enhance the practical utility of LLMs in software development while overcoming their inherent limitations in compositional reasoning and context handling.