LLMs for Generating and Evaluating Counterfactuals: A Comprehensive Study

Learning word vectors for sentiment analysis

Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011

Earlier work this paper cites.

A large annotated corpus for learning natural language inference

Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015

Earlier work this paper cites.

Breaking NLI systems with sentences that require simple lexical inferences

Max Glockner, Vered Shwartz, and Yoav Goldberg. 2018 · 2018

Earlier work this paper cites.

Explanation in artificial intelligence: Insights from the social sciences

Tim Miller. 2019 · 2019

Earlier work this paper cites.

Language Models are Unsupervised Multitask Learners

Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019

Earlier work this paper cites.

Sentence-BERT: Sentence embeddings using Siamese BERT-networks

Nils Reimers and Iryna Gurevych. 2019 · 2019

Earlier work this paper cites.

Evaluating models’ local decision boundaries via contrast sets

Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, and Ben Zhou. 2020 · 2020

Earlier work this paper cites.

Learning the difference that makes a difference with counterfactually-augmented data

Divyansh Kaushik, Eduard Hovy, and Zachary Lipton. 2020 · 2020

Earlier work this paper cites.

TextAttack: A framework for adversarial attacks, data augmentation, and adversarial training in NLP

John Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, and Yanjun Qi. 2020 · 2020

Earlier work this paper cites.

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 · 2020

Earlier work this paper cites.

Generating realistic natural language counterfactuals

Marcel Robeer, Floris Bex, and Ad Feelders. 2021 · 2021

Earlier work this paper cites.

Explaining NLP models via minimal contrastive editing (MiCE)

Alexis Ross, Ana Marasović, and Matthew Peters. 2021 · 2021

Earlier work this paper cites.

How does counterfactually augmented data impact models for social computing constructs?

Indira Sen, Mattia Samory, Fabian Flöck, Claudia Wagner, and Isabelle Augenstein. 2021 · 2021

Earlier work this paper cites.

Learning from the worst: Dynamically generated datasets to improve online hate detection

Bertie Vidgen, Tristan Thrush, Zeerak Waseem, and Douwe Kiela. 2021 · 2021

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Polyjuice: Generating counterfactuals for explaining, evaluating, and improving models

Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, and Daniel Weld. 2021 · 2021

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A survey on in-context learning

Original

Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui. 2022 · 2022

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What makes good in-context examples for GPT-3?

Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2022 · 2022

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Interpretable Machine Learning , 2 edition

Christoph Molnar. 2022 · 2022

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Benchmarking large language model capabilities for conditional generation

Joshua Maynez, Priyanka Agrawal, and Sebastian Gehrmann. 2023 · 2023

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Llama 2: Open foundation and fine-tuned chat models

Original

Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, and Moya Chen et. al. 2023 · 2023

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CREST: A joint framework for rationalization and counterfactual text generation

Marcos Treviso, Alexis Ross, Nuno M. Guerreiro, and André Martins. 2023 · 2023

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A survey of large language models

Original

Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023 · 2023

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Judging llm-as-a-judge with mt-bench and chatbot arena