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Identifying Variables Eval #1488

Merged
merged 10 commits into from
Mar 19, 2024
Merged

Identifying Variables Eval #1488

merged 10 commits into from
Mar 19, 2024

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thesofakillers
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@thesofakillers thesofakillers commented Mar 15, 2024

@JunShern will review this

Thank you for contributing an eval! ♥️

🚨 Please make sure your PR follows these guidelines, failure to follow the guidelines below will result in the PR being closed automatically. Note that even if the criteria are met, that does not guarantee the PR will be merged nor GPT-4 access be granted. 🚨

PLEASE READ THIS:

In order for a PR to be merged, it must fail on GPT-4. We are aware that right now, users do not have access, so you will not be able to tell if the eval fails or not. Please run your eval with GPT-3.5-Turbo, but keep in mind as we run the eval, if GPT-4 gets higher than 90% on the eval, we will likely reject it since GPT-4 is already capable of completing the task.

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Eval details 📑

Eval name

Identifying variables

Eval description

This eval tests how well models can determine what should be treated as the independent, dependent, and control variables for an experiment that tests a particular hypothesis, given some observational context.

What makes this a useful eval?

[Insert why this eval is worth including and any additional context]

Criteria for a good eval ✅

Below are some of the criteria we look for in a good eval. In general, we are seeking cases where the model does not do a good job despite being capable of generating a good response (note that there are some things large language models cannot do, so those would not make good evals).

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  • Thematically consistent: The eval should be thematically consistent. We'd like to see a number of prompts all demonstrating some particular failure mode. For example, we can create an eval on cases where the model fails to reason about the physical world.
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  • Includes good signal around what is the right behavior. This means either a correct answer for Basic evals or the Fact Model-graded eval, or an exhaustive rubric for evaluating answers for the Criteria Model-graded eval.
  • Include at least 15 high-quality examples.

If there is anything else that makes your eval worth including, please document it below.

Unique eval value

Insert what makes your eval high quality that was not mentioned above. (Not required)

Eval structure 🏗️

Your eval should

  • Check that your data is in evals/registry/data/{name}
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Final checklist 👀

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Eval JSON data

Since we are using Git LFS, we are asking eval submitters to add in as many Eval Samples (at least 5) from their contribution here:

View evals in JSON

Eval

INSERT_EVAL_HERE

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@JunShern JunShern self-requested a review March 19, 2024 14:19
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This looks ready to go now, thanks for the interesting eval!

@JunShern JunShern merged commit c207dba into openai:main Mar 19, 2024
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2 participants