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The TrustML Young Scientist Seminars (TrustML YSS) started from January 28, 2022.
The TrustML YSS is a video series that features young scientists giving talks and discoveries in relation with Trustworthy Machine Learning.
For more information please see the following site.
This network is funded by RIKEN-AIP's subsidy and JST, ACT-X Grant Number JPMJAX21AF, Japan.
【The 29th Seminar】
Date and Time: September 2nd 10:00 am - 11:00 am(JST)
Venue: Zoom webinar
Speaker: Kawin Ethayarajh (Stanford University)
Title: Understanding Dataset Difficulty with V-Usable Information
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 -- as the lack of V-usable information (Xu et al., 2019), where a lower value indicates a more difficult dataset for the model family V. Our framework allows for many types of comparisons under the same umbrella: not only can we compare different model families, but also different datasets, different slices of the same dataset, different instances in a distribution, and different input attributes. We apply our framework to discover annotation artefacts in widely-used NLP benchmarks, such as SNLI and CoLA.
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Please see the URL below.
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