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Has the code-generation capabilities of different LLMs been validated through formal benchmarking?

Generation Details

Date LLM Access Method
Nov 2024 GPT-4 Web UI

LLM Code Generation Benchmarking

Yes, there have been credible and independent benchmarking tests that assess the ability of various LLMs to perform specific programming tasks, such as generating Python code. Here are some key benchmarks:

  1. HumanEval: This benchmark consists of 164 hand-written programming problems covering a wide range of difficulties and domains, including math, string manipulation, data structures, and algorithms. It evaluates LLMs by providing function signatures and docstrings as prompts, requiring the models to generate the corresponding function bodies. The generated code is tested against predefined test cases to determine correctness[6].
  2. MBPP (Mostly Basic Python Problems): This benchmark includes 974 entry-level Python problems. It is designed to evaluate the code synthesis capabilities of LLMs from natural language descriptions[2].
  3. PythonSaga: A newer benchmark that addresses limitations in existing benchmarks by providing a balanced representation of 38 programming concepts across diverse difficulty levels. It features 185 hand-crafted prompts[2].
  4. SWE-bench: Contains over 2000 real-world GitHub issues and PRs from popular Python repositories. It evaluates LLMs on their ability to understand issue descriptions and coordinate changes across multiple functions and files[3].
  5. ClassEval: Focuses on class-level code generation tasks, requiring models to generate multiple interdependent methods based on natural language descriptions[5].

These benchmarks use metrics like Pass@k, which measures the success rate of generating code that passes all provided unit tests, offering a comprehensive evaluation of LLM performance in coding tasks.

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.