Explaining Language Models' Predictions with High-Impact Concepts

3 May 2023

Papers citing "Explaining Language Models' Predictions with High-Impact Concepts"

7 / 7 papers shown

Title
On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMs Nitay Calderon Roi Reichart 40 10 0 27 Jul 2024
Explaining black box text modules in natural language with language models Chandan Singh Aliyah R. Hsu Richard Antonello Shailee Jain Alexander G. Huth Bin-Xia Yu Jianfeng Gao MILM 31 47 0 17 May 2023
Probing Classifiers: Promises, Shortcomings, and Advances Yonatan Belinkov 226 405 0 24 Feb 2021
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan Ö. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 122 297 0 17 Oct 2019
What you can cram into a single vector: Probing sentence embeddings for linguistic properties Alexis Conneau Germán Kruszewski Guillaume Lample Loïc Barrault Marco Baroni 201 882 0 03 May 2018
A causal framework for explaining the predictions of black-box sequence-to-sequence models David Alvarez-Melis Tommi Jaakkola CML 229 201 0 06 Jul 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 254 3,684 0 28 Feb 2017