{"url":"/dataset/copen","name":"COPEN","full_name":"COnceptual knowledge Probing bENchmark","description_markdown":"COPEN is a COnceptual knowledge Probing benchmark that aims to analyze the conceptual understanding capabilities of Pre-trained Language Models (PLMs). Specifically, COPEN consists of three tasks:\r\n\r\n1. Conceptual Similarity Judgment (CSJ). Given a query entity and several candidate entities, the CSJ task requires selecting the most conceptually similar candidate entity to the query entity.\r\n2. Conceptual Property Judgment (CPJ). Given a statement describing a property of a concept, PLMs need to judge whether the statement is true.\r\n3. Conceptualization in Contexts (CiC). Given a sentence, an entity mentioned in the sentence, and several concept chains of the entity, PLMs need to select the most appropriate concept according to the context of the entity.","description_withheld":null,"homepage":"https://github.com/THU-KEG/COPEN","introduced_date":"2022-11-08","introduced_date_note":null,"introduced_by":{"paper":"/paper/copen-probing-conceptual-knowledge-in-pre","title":"COPEN: Probing Conceptual Knowledge in Pre-trained Language Models","first_author":"Hao Peng","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["COPEN"],"data_loaders":[],"num_papers_in_archive":3,"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."}