{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/information-theoretic-measures-of-dataset","title":"Understanding Dataset Difficulty with $\\mathcal{V}$-Usable Information","arxiv_id":"2110.08420","date":"2021-10-16","proceeding":null,"authors":["Kawin Ethayarajh","Yejin Choi","Swabha Swayamdipta"],"abstract":"Estimating the difficulty of a dataset typically involves comparing state-of-the-art models to humans; the bigger the performance gap, the harder the dataset is said to be. However, this comparison provides little understanding of how difficult each instance in a given distribution is, or what attributes make the dataset difficult for a given model. To address these questions, we frame dataset difficulty -- w.r.t. a model $\\mathcal{V}$ -- as the lack of $\\mathcal{V}$-$\\textit{usable information}$ (Xu et al., 2019), where a lower value indicates a more difficult dataset for $\\mathcal{V}$. We further introduce $\\textit{pointwise $\\mathcal{V}$-information}$ (PVI) for measuring the difficulty of individual instances w.r.t. a given distribution. While standard evaluation metrics typically only compare different models for the same dataset, $\\mathcal{V}$-$\\textit{usable information}$ and PVI also permit the converse: for a given model $\\mathcal{V}$, we can compare different datasets, as well as different instances/slices of the same dataset. Furthermore, our framework allows for the interpretability of different input attributes via transformations of the input, which we use to discover annotation artefacts in widely-used NLP benchmarks.","url_abs":"https://arxiv.org/abs/2110.08420v2","url_pdf":"https://arxiv.org/pdf/2110.08420v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"information-theoretic-measures-of-dataset","repo_url":"https://github.com/kawine/dataset_difficulty","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2110.08420","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}