title: Insights from Benchmarking Frontier Language Models on Web App Code
    Generation
summary: 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.
paper_authors._id: 66e00f0114d815f1ca94b35e
paper_authors.name: Yi Cui
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-10T19:10:04.971Z
paper_authors._id: 669dbd709a4bf63e08f1ddc2
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/669dbd709a4bf63e08f1ddc2/aV10ZJPPzH5LbnHFZNqc7.png
paper_authors.fullname: Yi Cui
paper_authors.type: user
paper_authors.user: onekq
paper_title: Insights from Benchmarking Frontier Language Models on Web App Code
    Generation
paper_summary: 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.
paper_ai_summary: Evaluation of large language models on the WebApp1K benchmark indicates varied performance in generating correct web application code, highlighting the need for improvements in model reliability.
paper_ai_keywords: large language models LLMs WebApp1K benchmark web application code lines of code LOC prompt engineering