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Sentiment is a pervasive feature in natural language text, yet it is an open question how sentiment is represented within Large Language Models (LLMs).
An Introduction to Systems Biology: Design Principles of Biological Circuits
Uri Alon · 2006
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Opinion mining and sentiment analysis
Bob Pang and Lillian Lee · 2008
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Sentiment analysis and opinion mining
Bing Liu · 2012
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Linguistic regularities in continuous space word representations
Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts · 2013
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Text analytics 2014: User perspectives on solutions and providers
Seth Grimes · 2014
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SemEval-2016 task 4: Sentiment analysis in Twitter
Preslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani, and Veselin Stoyanov · 2016
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Learning to generate reviews and discovering sentiment, 2017
Alec Radford, Rafal Jozefowicz, and Ilya Sutskever · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Openwebtext corpus, 2019
Aaron Gokaslan and Vanya Cohen · 2019
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Language models are unsupervised multitask learners, 2019
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 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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Neural natural language inference models partially embed theories of lexical entailment and negation
Atticus Geiger, Kyle Richardson, and Christopher Potts · 2020
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Attention is not only a weight: Analyzing transformers with vector norms, 2020
Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, and Kentaro Inui · 2020
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interpreting gpt: the logit lens, 2020
nostalgebraist · 2020
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Zoom in: An introduction to circuits
Chris Olah, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, and Shan Carter · 2020
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Language through a prism: A spectral approach for multiscale language representations, 2020
Alex Tamkin, Dan Jurafsky, and Noah Goodman · 2020
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Causal mediation analysis for interpreting neural nlp: The case of gender bias, 2020
Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Simas Sakenis, Jason Huang, Yaron Singer, and Stuart Shieber · 2020
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Can language models encode perceptual structure without grounding? a case study in color, 2021
Mostafa Abdou, Artur Kulmizev, Daniel Hershcovich, Stella Frank, Ellie Pavlick, and Anders Søgaard · 2021
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An interpretability illusion for bert, 2021
Tolga Bolukbasi, Adam Pearce, Ann Yuan, Andy Coenen, Emily Reif, Fernanda Viégas, and Martin Wattenberg · 2021
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A mathematical framework for transformer circuits
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah · 2021
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A mathematical framework for transformer circuits
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah · 2021
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Causal abstractions of neural networks
Sparks of artificial general intelligence: Early experiments with gpt-4, 2023
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, and Yi Zhang · 2023
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Causal scrubbing: a method for rigorously testing interpretability hypotheses [redwood research]
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Towards automated circuit discovery for mechanistic interpretability, 2023
Arthur Conmy, Augustine N. Mavor-Parker, Aengus Lynch, Stefan Heimersheim, and Adrià Garriga-Alonso · 2023
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Atticus Geiger, Hanson Lu, Thomas Icard, and Christopher Potts · 2021
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Multimodal neurons in artificial neural networks
Gabriel Goh, Nick Cammarata †, Chelsea Voss †, Shan Carter, Michael Petrov, Ludwig Schubert, Alec Radford, and Chris Olah · 2021
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Implicit representations of meaning in neural language models, 2021
Belinda Z. Li, Maxwell Nye, and Jacob Andreas · 2021
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DynaSent: A dynamic benchmark for sentiment analysis
Christopher Potts, Zhengxuan Wu, Atticus Geiger, and Douwe Kiela · 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 Gat, Atticus Geiger, Christopher Potts, Roi Reichart, and Zhengxuan Wu · 2022
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Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt · 2022
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Toy models of superposition
Nelson Elhage, Tristan Hume, Catherine Olsson, Nicholas Schiefer, Tom Henighan, Shauna Kravec, Zac Hatfield-Dodds, Robert Lasenby, Dawn Drain, Carol Chen, Roger Grosse, Sam McCandlish, Jared Kaplan, Dario Amodei, Martin Wattenberg, and Christopher Olah · 2022
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Inducing causal structure for interpretable neural networks
Atticus Geiger, Zhengxuan Wu, Hanson Lu, Josh Rozner, Elisa Kreiss, Thomas Icard, Noah Goodman, and Christopher Potts · 2022
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Survey on sentiment analysis: evolution of research methods and topics
J. Cui, Z. Wang, SB. Ho, et al · 2023
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SemEval-2017 task 4: Sentiment analysis in Twitter
Sara Rosenthal, Noura Farra, and Preslav Nakov · 2088
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