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How can python generation llms be evaluated

# What are some objective ways LLMs can be compared and evaluated for generating compliant Python?

Generation Details

Date LLM Access Method
Nov 2024 GPT-4 Web UI

Based on the search results and available information, there have indeed been several benchmarks and studies that have compared the capabilities of different LLMs in generating Python code from natural language descriptions. Here are some key points:

  1. HumanEval Benchmark: HumanEval is one of the most widely recognized benchmarks for evaluating Python code generation capabilities of LLMs [3][5]. It consists of 164 handwritten problems that require models to generate functional Python code based on natural language descriptions and function signatures [4].
  2. MBPP (Mostly Basic Python Programming) Benchmark: MBPP is another popular benchmark containing 974 entry-level Python programming problems [2][4]. It's often used alongside HumanEval to assess code generation capabilities.
  3. PythonSaga: This is a newer benchmark proposed to address limitations in existing benchmarks. It features 185 hand-crafted prompts covering 38 programming concepts across diverse difficulty levels [2].
  4. Comparative Studies: Several studies have used these benchmarks to compare different LLMs:
  5. A study comparing ChatGPT and GitHub Copilot found that ChatGPT outperformed Copilot in terms of correctness for algorithm code generation, achieving 79.17% correctness for Python compared to Copilot's 62.50% [1].
  6. Another study evaluated GPT-3.5, GPT-4, Bard, and Gemini across various coding tasks, including Python generation [5].
  7. BigCodeBench: Described as the "next generation of HumanEval," this benchmark contains 1140 function-level tasks for Python code generation, with an average of 5.6 test cases per example [3].
  8. Limitations and Challenges: Researchers have noted that existing benchmarks may have limitations, such as bias towards certain programming concepts or an overrepresentation of easy tasks [2]. This has led to efforts to create more comprehensive and balanced benchmarks.
  9. Metrics: Common metrics used in these benchmarks include Pass@k (especially Pass@1), which measures the rate of successfully passing provided unit tests [3][4].

While there doesn't seem to be a single, definitive survey comparing all LLMs for Python code generation, the benchmarks mentioned above (especially HumanEval and MBPP) are frequently used in the literature to compare different models' performance.

For the most up-to-date comparisons, you might want to look at recent papers or blog posts from AI research organizations, as the field is rapidly evolving and new models are constantly being released and evaluated.

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.