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As language models (LMs) deliver increasing performance on a range of NLP tasks, probing classifiers have become an indispensable technique in the effort to better understand their inner workings.
The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations
Reuben M Baron and David A Kenny · 1986
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Models, reasoning and inference
Judea Pearl et al · 2000
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Identifiability of path-specific effects
Chen Avin, Ilya Shpitser, and Judea Pearl · 2005
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Locating Causal Structure in Language
Steven Sloman · 2005
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A general approach to causal mediation analysis
Kosuke Imai, Luke Keele, and Dustin Tingley · 2010
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An introduction to causal inference
Judea Pearl · 2010
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Does string-based neural mt learn source syntax?
Xing Shi, Inkit Padhi, and Kevin Knight · 2016
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Fine-grained analysis of sentence embeddings using auxiliary prediction tasks
Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, and Yoav Goldberg · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Understanding intermediate layers using linear classifier probes, 2018
Guillaume Alain and Yoshua Bengio · 2018
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Analysis Methods in Neural Language Processing: A Survey
Yonatan Belinkov and James Glass · 2019
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Designing and interpreting probes with control tasks
John Hewitt and Percy Liang · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Path-specific effects
Naftali Weinberger · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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BERTering RAMS: What and how much does BERT already know about event arguments? - a study on the RAMS dataset
Varun Gangal and Eduard Hovy · 2020
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Learning the difference that makes a difference with counterfactually-augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary Lipton · 2020
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A tale of a probe and a parser
Rowan Hall Maudslay, Josef Valvoda, Tiago Pimentel, Adina Williams, and Ryan Cotterell · 2020
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Information-theoretic probing for linguistic structure
Tiago Pimentel, Josef Valvoda, Rowan Hall Maudslay, Ran Zmigrod, Adina Williams, and Ryan Cotterell · 2020
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Investigating gender bias in language models using causal mediation analysis
Toward causal representation learning
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio · 2021
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CEBab: Estimating the causal effects of real-world concepts on NLP model behavior
Eldar David Abraham, Karel D’Oosterlinck, Amir Feder, Yair Ori Gat, Atticus Geiger, Christopher Potts, Roi Reichart, and Zhengxuan Wu · 2022
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Language models as agent models
Jacob Andreas · 2022
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Probing classifiers: Promises, shortcomings, and advances
Yonatan Belinkov · 2022
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Measuring causal effects of data statistics on language model’sfactual’predictions
Yanai Elazar, Nora Kassner, Shauli Ravfogel, Amir Feder, Abhilasha Ravichander, Marius Mosbach, Yonatan Belinkov, Hinrich Schütze, and Yoav Goldberg · 2022
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Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, and Stuart Shieber · 2020
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Information-theoretic probing with minimum description length
Elena Voita and Ivan Titov · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush · 2020
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Can language models encode perceptual structure without grounding? a case study in color
Mostafa Abdou, Artur Kulmizev, Daniel Hershcovich, Stella Frank, Ellie Pavlick, and Anders Søgaard · 2021
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Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals
Yanai Elazar, Shauli Ravfogel, Alon Jacovi, and Yoav Goldberg · 2021
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Causal abstractions of neural networks
Atticus Geiger, Hanson Lu, Thomas F Icard, and Christopher Potts · 2021
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Implicit representations of meaning in neural language models
Belinda Z. Li, Maxwell Nye, and Jacob Andreas · 2021
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Causal inference in natural language processing: Estimation, prediction, interpretation and beyond
Amir Feder, Katherine A Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E Roberts, et al · 2022
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Probing as quantifying inductive bias
Alexander Immer, Lucas Torroba Hennigen, Vincent Fortuin, and Ryan Cotterell · 2022
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Emergent world representations: Exploring a sequence model trained on a synthetic task
Kenneth Li, Aspen K Hopkins, David Bau, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg · 2022
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Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov · 2022
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Naturalistic Causal Probing for Morpho-Syntax
Afra Amini, Tiago Pimentel, Clara Meister, and Ryan Cotterell · 2023
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Evidence of meaning in language models trained on programs, 2023
Charles Jin and Martin Rinard · 2023
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Codegen: An open large language model for code with multi-turn program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong · 2023
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An information theoretic view on selecting linguistic probes
Zining Zhu and Frank Rudzicz · 2023
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