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Feature attribution (FA) methods are common post-hoc approaches that explain how Large Language Models (LLMs) make predictions.
“what is relevant in a text document?”: An interpretable machine learning approach
Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek · 1932
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Quantifying attention flow in transformers, 2020
Samira Abnar and Willem Zuidema · 2005
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Learning to faithfully rationalize by construction, 2020
Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, and Byron C. Wallace · 2005
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Using “annotator rationales” to improve machine learning for text categorization
Omar Zaidan, Jason Eisner, and Christine Piatko · 2007
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Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus · 2014
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Neural text generation from structured data with application to the biography domain, 2016
Remi Lebret, David Grangier, and Michael Auli · 2016
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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola · 2016
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”why should i trust you?”: Explaining the predictions of any classifier, 2016
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Towards a rigorous science of interpretable machine learning, 2017
Finale Doshi-Velez and Been Kim · 2017
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A unified approach to interpreting model predictions, 2017
Scott Lundberg and Su-In Lee · 2017
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e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom · 2018
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The mythos of model interpretability
Zachary C. Lipton · 2018
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Nlize: A perturbation-driven visual interrogation tool for analyzing and interpreting natural language inference models
Shusen Liu, Zhimin Li, Tao Li, Vivek Srikumar, Valerio Pascucci, and Peer-Timo Bremer · 2018
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Comparing automatic and human evaluation of local explanations for text classification
Dong Nguyen · 2018
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Interpretable neural predictions with differentiable binary variables
Jasmijn Bastings, Wilker Aziz, and Ivan Titov · 2019
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Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, 2019
Cynthia Rudin · 2019
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Is attention interpretable?
Sofia Serrano and Noah A. Smith · 2019
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Learning important features through propagating activation differences, 2019
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2019
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A diagnostic study of explainability techniques for text classification
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, and Isabelle Augenstein · 2020
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RANCC: Rationalizing neural networks via concept clustering
Housam Khalifa Bashier, Mi-Young Kim, and Randy Goebel · 2020
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ERASER: A benchmark to evaluate rationalized NLP models
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C. Wallace · 2020
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Interpreting predictions of NLP models
Eric Wallace, Matt Gardner, and Sameer Singh · 2020
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Enjoy the salience: Towards better transformer-based faithful explanations with word salience
George Chrysostomou and Nikolaos Aletras · 2021
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Peter Hase, Harry Xie, and Mohit Bansal · 2021
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Potsawee Manakul, Adian Liusie, and Mark J. F. Gales · 2023
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Locating and editing factual associations in gpt, 2023
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov · 2023
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Instruction tuning with gpt-4, 2023
Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao · 2023
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Inseq: An interpretability toolkit for sequence generation models
Gabriele Sarti, Nils Feldhus, Ludwig Sickert, Oskar van der Wal, Malvina Nissim, and Arianna Bisazza · 2023
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Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto · 2023
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Maksims Ivanovs, Roberts Kadikis, and Kaspars Ozols · 2021
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Aligning faithful interpretations with their social attribution
Alon Jacovi and Yoav Goldberg · 2021
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Attention vs non-attention for a shapley-based explanation method
Tom Kersten, Hugh Mee Wong, Jaap Jumelet, and Dieuwke Hupkes · 2021
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Rationales for sequential predictions, 2021
Keyon Vafa, Yuntian Deng, David M. Blei, and Alexander M. Rush · 2021
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UNIREX: A unified learning framework for language model rationale extraction
Aaron Chan, Maziar Sanjabi, Lambert Mathias, Liang Tan, Shaoliang Nie, Xiaochang Peng, Xiang Ren, and Hamed Firooz · 2022
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A comparative study of faithfulness metrics for model interpretability methods
Chun Sik Chan, Huanqi Kong, and Liang Guanqing · 2022
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An empirical study on explanations in out-of-domain settings
George Chrysostomou and Nikolaos Aletras · 2022
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Llama: Open and efficient foundation language models, 2023
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample · 2023
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Attention is all you need, 2023
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Wizardlm: Empowering large language models to follow complex instructions, 2023
Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, and Daxin Jiang · 2023
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Incorporating attribution importance for improving faithfulness metrics
Zhixue Zhao and Nikolaos Aletras · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena, 2023
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica · 2023
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Alpagasus: Training a better alpaca with fewer data, 2024
Lichang Chen, Shiyang Li, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srinivasan, Tianyi Zhou, Heng Huang, and Hongxia Jin · 2024
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Self-alignment with instruction backtranslation, 2024
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Infobench: Evaluating instruction following ability in large language models, 2024
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DeepSeek-AI · 2025
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