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Interpretability tools that offer explanations in the form of a dialogue have demonstrated their efficacy in enhancing users' understanding (Slack et al., 2023; Shen et al., 2023), as one-off explanations may fall short in providing sufficient information to the user.
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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
Earlier work this paper cites.
Wordnet: A lexical database for english
George A. Miller. 1995 · 1995
Earlier work this paper cites.
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Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014 · 2014
Earlier work this paper cites.
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Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
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Earlier work this paper cites.
"why should i trust you?": Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Earlier work this paper cites.
SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation
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Earlier work this paper cites.
Axiomatic attribution for deep networks
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Earlier work this paper cites.
Evaluating semantic parsing against a simple web-based question answering model
Alon Talmor, Mor Geva, and Jonathan Berant. 2017 · 2017
Earlier work this paper cites.
e-SNLI: Natural language inference with natural language explanations
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Earlier work this paper cites.
SemEval-2018 task 11: Machine comprehension using commonsense knowledge
Simon Ostermann, Michael Roth, Ashutosh Modi, Stefan Thater, and Manfred Pinkal. 2018 · 2018
Earlier work this paper cites.
Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, and Dragomir Radev. 2018 · 2018
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.
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Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019 · 2019
Earlier work this paper cites.
What are people doing about XAI user experience? a survey on AI explainability research and practice
Juliana J. Ferreira and Mateus S. Monteiro. 2020 · 2020
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive NLP tasks
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Earlier work this paper cites.
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Ian Tenney, James Wexler, Jasmijn Bastings, Tolga Bolukbasi, Andy Coenen, Sebastian Gehrmann, Ellen Jiang, Mahima Pushkarna, Carey Radebaugh, Emily Reif, and Ann Yuan. 2020 · 2020
Earlier work this paper cites.
Fairseq S2T: Fast speech-to-text modeling with fairseq
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Earlier work this paper cites.
Neural text generation with unlikelihood training
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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.
Explanations for CommonsenseQA: New Dataset and Models
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Earlier work this paper cites.
CrossCheck: Rapid, reproducible, and interpretable model evaluation
Dustin Arendt, Zhuanyi Shaw, Prasha Shrestha, Ellyn Ayton, Maria Glenski, and Svitlana Volkova. 2021 · 2021
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A conversational interface for interacting with machine learning models
Davide Carneiro, Patrícia Veloso, Miguel Guimarães, Joana Baptista, and Miguel Sousa. 2021 · 2021
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Alexis Ross, Ana Marasović, and Matthew Peters. 2021 · 2021
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COVID-fact: Fact extraction and verification of real-world claims on COVID-19 pandemic
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Constrained language models yield few-shot semantic parsers
Richard Shin, Christopher Lin, Sam Thomson, Charles Chen, Subhro Roy, Emmanouil Antonios Platanios, Adam Pauls, Dan Klein, Jason Eisner, and Benjamin Van Durme. 2021 · 2021
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AugGPT: Leveraging chatGPT for text data augmentation
Haixing Dai, Zhengliang Liu, Wenxiong Liao, Xiaoke Huang, Yihan Cao, Zihao Wu, Lin Zhao, Shaochen Xu, Wei Liu, Ninghao Liu, Sheng Li, Dajiang Zhu, Hongmin Cai, Lichao Sun, Quanzheng Li, Dinggang Shen, Tianming Liu, and Xiang Li. 2023 · 2023
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Sequential integrated gradients: a simple but effective method for explaining language models
Joseph Enguehard. 2023 · 2023
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InterroLang: Exploring NLP models and datasets through dialogue-based explanations
Nils Feldhus, Qianli Wang, Tatiana Anikina, Sahil Chopra, Cennet Oguz, and Sebastian Möller. 2023 · 2023
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OPTQ: Accurate quantization for generative pre-trained transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. 2023 · 2023
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PromptSource: An integrated development environment and repository for natural language prompts
Stephen Bach, Victor Sanh, Zheng Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M Saiful Bari, Thibault Fevry, Zaid Alyafeai, Manan Dey, Andrea Santilli, Zhiqing Sun, Srulik Ben-david, Canwen Xu, Gunjan Chhablani, Han Wang, Jason Fries, Maged Al-shaibani, Shanya Sharma, Urmish Thakker, Khalid Almubarak, Xiangru Tang, Dragomir Radev, Mike Tian-jian Jiang, and Alexander Rush. 2022 · 2022
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A survey on automated fact-checking
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Large language models are zero-shot reasoners
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Rethinking explainability as a dialogue: A practitioner’s perspective
Himabindu Lakkaraju, Dylan Slack, Yuxin Chen, Chenhao Tan, and Sameer Singh. 2022 · 2022
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Post-hoc interpretability for neural NLP: A survey
Andreas Madsen, Siva Reddy, and Sarath Chandar. 2022 · 2022
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Diagnosing AI explanation methods with folk concepts of behavior
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Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023 · 2023
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XMD: An end-to-end framework for interactive explanation-based debugging of NLP models
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MultiViz: Towards visualizing and understanding multimodal models
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Fairness-guided few-shot prompting for large language models
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ConvXAI: a system for multimodal interaction with any black-box explainer
Lorenzo Malandri, Fabio Mercorio, Mezzanzanica Mario, and Nobani Navid. 2023 · 2023
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IFAN: An explainability-focused interaction framework for humans and NLP models
Edoardo Mosca, Daryna Dementieva, Tohid Ebrahim Ajdari, Maximilian Kummeth, Kirill Gringauz, Yutong Zhou, and Georg Groh. 2023 · 2023
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Orca: Progressive learning from complex explanation traces of GPT-4
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The RefinedWeb dataset for Falcon LLM: Outperforming curated corpora with web data only
Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Hamza Alobeidli, Alessandro Cappelli, Baptiste Pannier, Ebtesam Almazrouei, and Julien Launay. 2023 · 2023
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Inseq: An interpretability toolkit for sequence generation models
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ConvXAI: Delivering heterogeneous AI explanations via conversations to support human-AI scientific writing
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Explaining machine learning models with interactive natural language conversations using TalkToModel
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Llama 2: Open foundation and fine-tuned chat models
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, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023 · 2023
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iSee: Intelligent sharing of explanation experience by users for users
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Large language models as optimizers
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Tong Zhang, X. Jessie Yang, and Boyang Li. 2023 · 2023
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