Insights from Benchmarking Frontier Language Models on Web App Code
  Generation
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.
66e00f0114d815f1ca94b35e
Yi Cui
claimed_verified
2024-09-10T19:10:04.971Z
669dbd709a4bf63e08f1ddc2
https://cdn-avatars.huggingface.co/v1/production/uploads/669dbd709a4bf63e08f1ddc2/aV10ZJPPzH5LbnHFZNqc7.png
Yi Cui
user
onekq
Insights from Benchmarking Frontier Language Models on Web App Code
  Generation
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.
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.
large language models LLMs WebApp1K benchmark web application code lines of code LOC prompt engineering