Summary of Mapcoder: Multi-agent Code Generation For Competitive Problem Solving, by Md. Ashraful Islam et al.
MapCoder: Multi-Agent Code Generation for Competitive Problem Solving
by Md. Ashraful Islam, Mohammed Eunus Ali, Md Rizwan Parvez
First submitted to arxiv on: 18 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces MapCoder, a new approach to code generation tasks that leverages multi-agent prompting. Unlike large language models (LLMs), which excel in natural language processing but struggle with code generation, MapCoder replicates the full cycle of program synthesis as observed in human developers. The framework consists of four LLM agents designed to emulate stages such as recalling relevant examples, planning, code generation, and debugging. Experiments were conducted using multiple LLM ablations and analyses across eight competitive problem-solving and program synthesis benchmarks, achieving state-of-the-art results on HumanEval (93.9%), MBPP (83.1%), APPS (22.0%), CodeContests (28.5%), and xCodeEval (45.3%). MapCoder consistently outperforms previous methods across various programming languages and problem difficulties. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MapCoder is a new way to generate code that uses multiple language models working together. This helps them understand complex problems and write correct code. The approach is inspired by how humans write code, breaking it down into steps like finding examples, planning, writing the code, and testing it. The researchers tested MapCoder on many different programming languages and problem types, and it did better than other methods in most cases. |
Keywords
» Artificial intelligence » Natural language processing » Prompting
