{"url":"/dataset/secqa","name":"SecQA","full_name":null,"description_markdown":"SecQA is a specialized dataset created for the evaluation of Large Language Models (LLMs) in the domain of computer security. It consists of multiple-choice questions, generated using GPT-4 and the [Computer Systems Security: Planning for Success](https://web.njit.edu/~rt494/security/) textbook, aimed at assessing the understanding and application of LLMs' knowledge in computer security.","description_withheld":null,"homepage":"https://huggingface.co/datasets/zefang-liu/secqa","introduced_date":"2023-12-26","introduced_date_note":null,"introduced_by":{"paper":"/paper/secqa-a-concise-question-answering-dataset","title":"SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security","first_author":"Zefang Liu","url":null},"license":{"name":"CC BY-NC-SA 4.0","url":"https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Multiple Choice Question Answering (MCQA)","url":"/task/multiple-choice-qa","datasets_with_task":"/datasets/task/multiple-choice-qa"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["SecQA"],"data_loaders":[],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}