{"url":"/dataset/scieval","name":"SciEval","full_name":null,"description_markdown":"**SciEval** is a comprehensive and multi-disciplinary evaluation benchmark designed to assess the performance of **large language models (LLMs)** in the scientific domain. It addresses several critical issues related to evaluating LLMs for scientific research.\r\n\r\nHere are the key features of SciEval:\r\n\r\n1. **Multi-Dimensional Evaluation**: SciEval systematically evaluates scientific research ability across **four dimensions** based on **Bloom's taxonomy**. These dimensions cover various aspects of scientific understanding and reasoning.\r\n\r\n2. **Objective and Subjective Questions**: Unlike existing benchmarks that primarily rely on pre-collected objective questions, SciEval includes both **objective** and **subjective** questions. This approach ensures a more comprehensive evaluation of LLMs' abilities.\r\n\r\n3. **Dynamic Subset**: To prevent potential data leakage, SciEval introduces a **\"dynamic\" subset** based on scientific principles. This subset dynamically adapts to evaluate LLMs' performance without compromising the integrity of the evaluation process.","description_withheld":null,"homepage":"https://github.com/OpenDFM/SciEval","introduced_date":"2023-08-25","introduced_date_note":null,"introduced_by":{"paper":"/paper/scieval-a-multi-level-large-language-model","title":"SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Research","first_author":"Liangtai Sun","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["SciEval"],"data_loaders":[],"num_papers_in_archive":13,"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":"2026-09-25T09:33:49+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."}