A Fine-grained Interpretability Evaluation Benchmark for Neural NLP

23 May 2022

Papers citing "A Fine-grained Interpretability Evaluation Benchmark for Neural NLP"

9 / 9 papers shown

Title
Inpainting the Gaps: A Novel Framework for Evaluating Explanation Methods in Vision Transformers Lokesh Badisa Sumohana S. Channappayya 52 0 0 17 Jun 2024
Data Science Principles for Interpretable and Explainable AI Kris Sankaran FaML 52 0 0 17 May 2024
Explainable Causal Analysis of Mental Health on Social Media Data Chandni Saxena Muskan Garg G. Saxena CML 39 8 0 16 Oct 2022
Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies Mor Geva Daniel Khashabi Elad Segal Tushar Khot Dan Roth Jonathan Berant RALM 259 682 0 06 Jan 2021
e-SNLI: Natural Language Inference with Natural Language Explanations Oana-Maria Camburu Tim Rocktaschel Thomas Lukasiewicz Phil Blunsom LRM 287 625 0 04 Dec 2018
Generating Natural Language Adversarial Examples M. Alzantot Yash Sharma Ahmed Elgohary Bo-Jhang Ho Mani B. Srivastava Kai-Wei Chang AAML 258 916 0 21 Apr 2018
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 304 7,005 0 20 Apr 2018
A causal framework for explaining the predictions of black-box sequence-to-sequence models David Alvarez-Melis Tommi Jaakkola CML 235 203 0 06 Jul 2017
A Survey on Deep Learning in Medical Image Analysis G. Litjens Thijs Kooi B. Bejnordi A. Setio F. Ciompi Mohsen Ghafoorian Jeroen van der Laak Bram van Ginneken C. I. Sánchez OOD 361 10,639 0 19 Feb 2017