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Computer Science Theory #83
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Thank you for the contribution!
We're having some trouble loading the YAML, I think you have an extra space before computer-science-problems.s1.simple-v0
.
Could you please fix this and also merge the latest changes to main? We'd be able to test for any other errors using the CI.
Thanks!
Should be all good now- apologies! |
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Looks good now! Great eval, we're getting about 63% on GPT4.
We've approved the PR and will merge. We'll follow up shortly with details on priority access to GPT-4 API.
# 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 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 since GPT-4 is already capable of completing the task. We plan to roll out a way for users submitting evals to see the eval performance on GPT-4 soon. Stay tuned! Until then, you will not be able to see the eval performance on GPT-4. We encourage partial PR's with ~5-10 example that we can then run the evals on and share the results with you so you know how your eval does with GPT-4 before writing all 100 examples. ## Eval details 📑 ### Eval name Computer Science based questions ### Eval description Testing the models ability to answer multiple choice computer science questions correctly ### What makes this a useful eval? Tests whether it has the ability to answer time complexity, binary tree, algorithmic computer science calculations. ## 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). Your eval should be: - [X] 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. - [X] Contains failures where a human can do the task, but either GPT-4 or GPT-3.5-Turbo could not. - [X] 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 100 high quality examples (it is okay to only contribute 5-10 meaningful examples and have us test them with GPT-4 before adding all 100) 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 - [X] Check that your data is in `evals/registry/data/{name}` - [X] Check that your yaml is registered at `evals/registry/evals/{name}.jsonl` - [X] Ensure you have the right to use the data you submit via this eval (For now, we will only be approving evals that use one of the existing eval classes. You may still write custom eval classes for your own cases, and we may consider merging them in the future.) ## Final checklist 👀 ### Submission agreement By contributing to Evals, you are agreeing to make your evaluation logic and data under the same MIT license as this repository. You must have adequate rights to upload any data used in an Eval. OpenAI reserves the right to use this data in future service improvements to our product. Contributions to OpenAI Evals will be subject to our usual Usage Policies (https://platform.openai.com/docs/usage-policies). - [X] I agree that my submission will be made available under an MIT license and complies with OpenAI's usage policies. ### Email address validation If your submission is accepted, we will be granting GPT-4 access to a limited number of contributors. Access will be given to the email address associated with the merged pull request. - [X] I acknowledge that GPT-4 access will only be granted, if applicable, to the email address used for my merged pull request. ### Limited availability acknowledgement We know that you might be excited to contribute to OpenAI's mission, help improve our models, and gain access to GPT-4. However, due to the requirements mentioned above and high volume of submissions, we will not be able to accept all submissions and thus not grant everyone who opens a PR GPT-4 access. We know this is disappointing, but we hope to set the right expectation before you open this PR. - [X] I understand that opening a PR, even if it meets the requirements above, does not guarantee the PR will be merged nor GPT-4 access granted. ### Submit eval - [X] I have filled out all required fields in the evals PR form - [x] (Ignore if not submitting code) I have run `pip install pre-commit; pre-commit install` and have verified that `black`, `isort`, and `autoflake` are running when I commit and push Failure to fill out all required fields will result in the PR being closed. ### 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: <details> <summary>View evals in JSON</summary> ### Eval ```jsonl {"input":[{"role":"system","content":"Choose the best multiple choice answer to this question. Reply ONLY with the single letter of the answer you have chosen."},{"role":"user","content":"How many children does a binary tree have? a) 2 b) any number of children c) 0 or 1 or 2 d) 0 or 1"}],"ideal":"c"} {"input":[{"role":"system","content":"Choose the best multiple choice answer to this question. Reply ONLY with the single letter of the answer you have chosen."},{"role":"user","content":"What is/are the disadvantages of implementing tree using normal arrays? a) difficulty in knowing children nodes of a node b) difficult in finding the parent of a node c) have to know the maximum number of nodes possible before creation of trees d) difficult to implement"}],"ideal":"c"} {"input":[{"role":"system","content":"Choose the best multiple choice answer to this question. Reply ONLY with the single letter of the answer you have chosen."},{"role":"user","content":"What must be the ideal size of array if the height of tree is ‘l’? a) (2^l)-1 b) l-1 c) l d) 2l"}],"ideal":"a"} {"input":[{"role":"system","content":"Choose the best multiple choice answer to this question. Reply ONLY with the single letter of the answer you have chosen."},{"role":"user","content":"What are the children for node ‘w’ of a complete-binary tree in an array representation? a) 2w and 2w+1 b) 2+w and 2-w c) w+1/2 and w/2 d) w-1/2 and w+1/2"}],"ideal":"a"} {"input":[{"role":"system","content":"Choose the best multiple choice answer to this question. Reply ONLY with the single letter of the answer you have chosen."},{"role":"user","content":"What is the parent for a node ‘w’ of a complete binary tree in an array representation when w is not 0? a) floor(w-1/2) b) ceil(w-1/2) c) w-1/2 d) w/2"}],"ideal":"a"} {"input":[{"role":"system","content":"Choose the best multiple choice answer to this question. Reply ONLY with the single letter of the answer you have chosen."},{"role":"user","content":"If the tree is not a complete binary tree then what changes can be made for easy access of children of a node in the array? a) every node stores data saying which of its children exist in the array b) no need of any changes continue with 2w and 2w+1, if node is at i c) keep a seperate table telling children of a node d) use another array parallel to the array with tree"}],"ideal":"a"} ``` </details>
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 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 since GPT-4 is already capable of completing the task.
We plan to roll out a way for users submitting evals to see the eval performance on GPT-4 soon. Stay tuned! Until then, you will not be able to see the eval performance on GPT-4. We encourage partial PR's with ~5-10 example that we can then run the evals on and share the results with you so you know how your eval does with GPT-4 before writing all 100 examples.
Eval details 📑
Eval name
Computer Science based questions
Eval description
Testing the models ability to answer multiple choice computer science questions correctly
What makes this a useful eval?
Tests whether it has the ability to answer time complexity, binary tree, algorithmic computer science calculations.
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).
Your eval should be:
Basic
evals or theFact
Model-graded eval, or an exhaustive rubric for evaluating answers for theCriteria
Model-graded eval.If there is anything else that makes your eval worth including, please document it below.
Unique eval value
Eval structure 🏗️
Your eval should
evals/registry/data/{name}
evals/registry/evals/{name}.jsonl
(For now, we will only be approving evals that use one of the existing eval classes. You may still write custom eval classes for your own cases, and we may consider merging them in the future.)
Final checklist 👀
Submission agreement
By contributing to Evals, you are agreeing to make your evaluation logic and data under the same MIT license as this repository. You must have adequate rights to upload any data used in an Eval. OpenAI reserves the right to use this data in future service improvements to our product. Contributions to OpenAI Evals will be subject to our usual Usage Policies (https://platform.openai.com/docs/usage-policies).
Email address validation
If your submission is accepted, we will be granting GPT-4 access to a limited number of contributors. Access will be given to the email address associated with the merged pull request.
Limited availability acknowledgement
We know that you might be excited to contribute to OpenAI's mission, help improve our models, and gain access to GPT-4. However, due to the requirements mentioned above and high volume of submissions, we will not be able to accept all submissions and thus not grant everyone who opens a PR GPT-4 access. We know this is disappointing, but we hope to set the right expectation before you open this PR.
Submit eval
pip install pre-commit; pre-commit install
and have verified thatblack
,isort
, andautoflake
are running when I commit and pushFailure to fill out all required fields will result in the PR being closed.
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