Papers Evaluated

Preliminary Dataset

Title	Authors	Publication Year
Post hoc explanations may be ineffective for detecting unknown spurious correlation	Julius Adebayo, Michael Muelly, Harold Abelson, Been Kim	2022
"What is relevant in a text document?": An interpretable machine learning approach	Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, Wojciech Samek	2017
Evaluating recurrent neural network explanations	Leila Arras, Ahmed Osman, Klaus-Robert Müller, Wojciech Samek	2019
A diagnostic study of explainability techniques for text classification	Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, Isabelle Augenstein	2020
Chain-of-thought unfaithfulness as disguised accuracy	Oliver Bentham, Nathan Stringham, Ana Marasović	2024
REV: information-theoretic evaluation of free-text rationales	Hanjie Chen, Faeze Brahman, Xiang Ren, Yangfeng Ji, Yejin Choi, Swabha Swayamdipta	2023
Reasoning Models Don't Always Say What They Think	Yanda Chen, Joe Benton, Ansh Radhakrishnan, Jonathan Uesato, Carson Denison, John Schulman, Arushi Somani, Peter Hase, Misha Wagner, Fabien Roger, Vlad Mikulik, Samuel R. Bowman, Jan Leike, Jared Kaplan, Ethan Perez	2025
Leakage-adjusted simulatability: Can models generate non-trivial explanations of their behavior in natural language?	Peter Hase, Shiyue Zhang, Harry Xie, Mohit Bansal	2020
A benchmark for interpretability methods in deep neural networks	Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, Been Kim	2019
Alignment rationale for natural language inference	Zhongtao Jiang, Yuanzhe Zhang, Zhao Yang, Jun Zhao, Kang Liu	2021