Guided Code Generation with LLMs: A Multi-Agent Framework for Complex Code Tasks

TL;DR

This paper presents a multi-agent framework for guided code generation using LLMs, significantly improving accuracy on complex tasks by structuring the generation process and leveraging LLMs' strengths.

Contribution

Introduces a novel agentic framework that enhances code generation by structuring tasks and guiding LLMs, addressing their limitations in reasoning and long-context understanding.

Findings

23.79% improvement in solution accuracy on HumanEval benchmark

Framework leverages LLMs as fuzzy searchers and information retrievers

Significantly enhances practical utility of LLMs in software development

Abstract

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.