Add ability to evauate multiple choice tasks (#5047)
* TruthfulQA: 1st attempt, does not look like it is working The same implementation can be used for HellaSwag as well, so I converted a HellaSwag validation dataset to the binary format used here and tested with that. The score is only around 50, so something is not quite right. * TruthfulQA: works but the result is bad I know it works because if I convert the HellaSwag validation data to the binary format used in the truthful_qa_score() function I get the exact same result as from the hellaswag_score() function. But I guess, the questions are tricky and the way I have done the combination of question + answer is very likely not the best. The TruthfulQA validation dataset contains 817 questions, with random chance result around 19%. With this version I get 29.1% for Mistral-7B and 55.2% for Mistral-7B-Instruct-v0.2. The HF leader board results for these two models are 42.2% and 68.3%, respectively. * TruthfulQA: fix random sample * TruthfulQA: prepare tasks in parallel for large test datasets * Rename truthful_qa to multiple_choice * Make MSVC happy I had forgotten that MSVC does not make constexpr's available inside a lambda. --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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3 changed files with 422 additions and 3 deletions
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@ -108,6 +108,9 @@ struct gpt_params {
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bool winogrande = false; // compute Winogrande score over random tasks from datafile supplied in prompt
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size_t winogrande_tasks= 0; // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed
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bool multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt
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size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed
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bool mul_mat_q = true; // if true, use mul_mat_q kernels instead of cuBLAS
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bool random_prompt = false; // do not randomize prompt if none provided
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bool use_color = false; // use color to distinguish generations and inputs
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