{"url":"/dataset/impact","name":"IMPACT","full_name":null,"description_markdown":"The IMPACT dataset contains 50 human created prompts for each category, 200 in total, to test LLMs general writing ability. Instructed LLMs demonstrate promising ability in writing-based tasks, such as composing letters or ethical debates. This dataset consists prompts across 4 diverse usage scenarios:\r\n- Informative Writing: User queries such as self-help advice or explanations for various concept\r\n- Professional Writing: Format involves suggestions presentations or emails in a business setting\r\n- Argumentative Writing: Debate positions on ethical and societal question\r\n- Creative Writing: Diverse writing formats such as stories, poems, and songs.","description_withheld":null,"homepage":"https://huggingface.co/datasets/declare-lab/InstructEvalImpact","introduced_date":"2023-06-07","introduced_date_note":null,"introduced_by":{"paper":"/paper/instructeval-towards-holistic-evaluation-of","title":"INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models","first_author":"Yew Ken Chia","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["IMPACT"],"data_loaders":[],"num_papers_in_archive":47,"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."}