基于对Web应用代码生成进行基准测试的前沿语言模型的见解

Insights from Benchmarking Frontier Language Models on Web App Code Generation

September 8, 2024

作者: Yi Cui

cs.AI

摘要

本文介绍了对16个前沿大型语言模型（LLMs）在WebApp1K基准测试上的评估结果。这个测试套件旨在评估LLMs生成Web应用程序代码的能力。结果显示，虽然所有模型具有类似的基础知识，但它们的性能差异在于它们所犯错误的频率。通过分析代码行数（LOC）和错误分布，我们发现编写正确的代码比生成错误的代码更复杂。此外，提示工程在减少错误方面的效果有限，除了特定情况。这些发现表明，进一步发展编码LLM应强调模型的可靠性和错误最小化。

English

This paper presents insights from evaluating 16 frontier large language models (LLMs) on the WebApp1K benchmark, a test suite designed to assess the ability of LLMs to generate web application code. The results reveal that while all models possess similar underlying knowledge, their performance is differentiated by the frequency of mistakes they make. By analyzing lines of code (LOC) and failure distributions, we find that writing correct code is more complex than generating incorrect code. Furthermore, prompt engineering shows limited efficacy in reducing errors beyond specific cases. These findings suggest that further advancements in coding LLM should emphasize on model reliability and mistake minimization.