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 #130

Short description:

• Test cases involving label images will now accept 0 as a valid label if the LLM solution includes it.

How do you think will this influence the benchmark results?

• Broadly increases the scoring for these test_cases (in my view, nearer to expectation)

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

• This one also depends a little on Prompt refinement (discussion about if this should be merged or not) #118. If our model prompting becomes more specific (e.g., to directly specify that 0 is not a valid label), this PR may be closed.