This PR contains:

• a new test-case for the benchmark
  • I hereby confirm that NO LLM-based technology (such as github copilot) was used while writing this benchmark
• new dependencies in requirements.txt
  • The environment.yml file was updated using the command conda env export > environment.yml
• new generator-functions allowing to sample from other LLMs
• new samples (sample_....jsonl files)
• new benchmarking results (..._results.jsonl files)
• documentation update
• bug fixes

Related github issue (if relevant): closes #124

Short description:

• Allows both the unbiased and unbiased standard deviation allowance

How do you think will this influence the benchmark results?

• Fixes many missed positive results for test_case 'mean_std_column.ipynb'

Why do you think it makes sense to merge this PR?

• Makes a more accurate benchmark according to the stated goal and prompt.