Towards Faithful Natural Language Explanations: A Study Using Activation Patching in Large Language Models

Explain yourself! leveraging language models for commonsense reasoning

Original

Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 1906

Earlier work this paper cites.

Eraser: A benchmark to evaluate rationalized nlp models

Original

Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C Wallace. 2019 · 1911

Earlier work this paper cites.

Towards faithfully interpretable nlp systems: How should we define and evaluate faithfulness?

Original

Alon Jacovi and Yoav Goldberg. 2020 · 2004

Earlier work this paper cites.

Causal mediation analysis for interpreting neural nlp: The case of gender bias

Original

Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Simas Sakenis, Jason Huang, Yaron Singer, and Stuart Shieber. 2020 · 2004

Earlier work this paper cites.

Semeval-2020 task 4: Commonsense validation and explanation

Original

Cunxiang Wang, Shuailong Liang, Yili Jin, Yilong Wang, Xiaodan Zhu, and Yue Zhang. 2020 · 2007

Earlier work this paper cites.

Measuring association between labels and free-text rationales

Original

Sarah Wiegreffe, Ana Marasović, and Noah A Smith. 2020 · 2010

Earlier work this paper cites.

Rationalizing neural predictions

Original

Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016

Earlier work this paper cites.

A unified approach to interpreting model predictions

Scott M Lundberg and Su-In Lee. 2017 · 2017

Earlier work this paper cites.

Attention is all you need

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017

Earlier work this paper cites.

e-snli: Natural language inference with natural language explanations

Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018

Earlier work this paper cites.

Metrics for explainable ai: Challenges and prospects

Original

Robert R Hoffman, Shane T Mueller, Gary Klein, and Jordan Litman. 2018 · 2018

Earlier work this paper cites.

The out-of-distribution problem in explainability and search methods for feature importance explanations

Peter Hase, Harry Xie, and Mohit Bansal. 2021 · 2021

Earlier work this paper cites.

Aligning faithful interpretations with their social attribution

Alon Jacovi and Yoav Goldberg. 2021 · 2021

Cited alongside, same era.

Locating and editing factual associations in gpt

Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022 · 2022

Cited alongside, same era.

Direct and indirect effects

Judea Pearl. 2022 · 2022

Cited alongside, same era.

Chain-of-thought prompting elicits reasoning in large language models

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022

Cited alongside, same era.

Gpt-4 technical report

Original

Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023

Cited alongside, same era.

Faithfulness tests for natural language explanations

On measuring faithfulness of natural language explanations

Original

Letitia Parcalabescu and Anette Frank. 2023 · 2023

Later among the works it cites.

A comprehensive review on financial explainable ai

Original

Wei Jie Yeo, Wihan van der Heever, Rui Mao, Erik Cambria, Ranjan Satapathy, and Gianmarco Mengaldo. 2023 · 2023

Later among the works it cites.

Towards best practices of activation patching in language models: Metrics and methods

Original

Fred Zhang and Neel Nanda. 2023 · 2023

Later among the works it cites.

Faithfulness vs. plausibility: On the (un) reliability of explanations from large language models

Original

Chirag Agarwal, Sree Harsha Tanneru, and Himabindu Lakkaraju. 2024 · 2024

Closest in time.

How interpretable are reasoning explanations from prompting large language models?