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Explaining the black-box predictions of NLP models naturally and accurately is an important open problem in natural language generation.
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 · 1901
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2019 · 1910
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An information bottleneck approach for controlling conciseness in rationale extraction
Bhargavi Paranjape, Mandar Joshi, John Thickstun, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2020 · 1952
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The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek. 2000 · 2000
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Manual and automatic evaluation of summaries
Chin-Yew Lin and Eduard Hovy. 2002 · 2002
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Explaining question answering models through text generation
Veronica Latcinnik and Jonathan Berant. 2020 · 2004
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky. 2015 · 2015
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Cider: Consensus-based image description evaluation
Ramakrishna Vedantam, C. Lawrence Zitnick, and Devi Parikh. 2015 · 2015
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What’s in an explanation? characterizing knowledge and inference requirements for elementary science exams
Peter Jansen, Niranjan Balasubramanian, Mihai Surdeanu, and Peter Clark. 2016 · 2016
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A diversity-promoting objective function for neural conversation models
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and William B Dolan. 2016 · 2016
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Deep variational information bottleneck
Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, and Kevin Murphy. 2017 · 2017
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Where is your evidence: Improving fact-checking by justification modeling
Tariq Alhindi, Savvas Petridis, and Smaranda Muresan. 2018 · 2018
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e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
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Does it make sense? and why? a pilot study for sense making and explanation
Cunxiang Wang, Shuailong Liang, Yue Zhang, Xiaonan Li, and Tian Gao. 2019 · 2019
Cited alongside, same era.
Bottlesum: Unsupervised and self-supervised sentence summarization using the information bottleneck principle
Peter West, Ari Holtzman, Jan Buys, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
Rethinking cooperative rationalization: Introspective extraction and complement control
Mo Yu, Shiyu Chang, Yang Zhang, and Tommi Jaakkola. 2019 · 2019
Cited alongside, same era.
Make up your mind! adversarial generation of inconsistent natural language explanations
Oana-Maria Camburu, Brendan Shillingford, Pasquale Minervini, Thomas Lukasiewicz, and Phil Blunsom. 2020 · 2020
Cited alongside, same era.
Union: An unreferenced metric for evaluating open-ended story generation
Jian Guan and Minlie Huang. 2020 · 2020
Cited alongside, same era.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022 · 2022
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GLM: general language model pretraining with autoregressive blank infilling
Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, and Jie Tang. 2022 · 2022
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Understanding dataset difficulty with V -usable information
Kawin Ethayarajh, Yejin Choi, and Swabha Swayamdipta. 2022 · 2022
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Learning to scaffold: Optimizing model explanations for teaching
Patrick Fernandes, Marcos Treviso, Danish Pruthi, André Martins, and Graham Neubig. 2022 · 2022
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News summarization and evaluation in the era of gpt-3
Tanya Goyal, Junyi Jessy Li, and Greg Durrett. 2022 · 2022
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Negated and misprimed probes for pretrained language models: Birds can talk, but cannot fly
Nora Kassner and Hinrich Schütze. 2020 · 2020
Cited alongside, same era.
Explainable automated fact-checking for public health claims
Neema Kotonya and Francesca Toni. 2020 · 2020
Cited alongside, same era.
A theory of usable information under computational constraints
Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, and Stefano Ermon. 2020 · 2020
Cited alongside, same era.
Bertscore: Evaluating text generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
Cited alongside, same era.
Explanations for commonsenseqa: New dataset and models
Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, Dinesh Khandelwal, Parag Singla, and Dinesh Garg. 2021 · 2021
Cited alongside, same era.
Learning to rationalize for nonmonotonic reasoning with distant supervision
Faeze Brahman, Vered Shwartz, Rachel Rudinconger, and Yejin Choi. 2021 · 2021
Cited alongside, same era.
Summarize-then-answer: Generating concise explanations for multi-hop reading comprehension
Naoya Inoue, Harsh Trivedi, Steven Sinha, Niranjan Balasubramanian, and Kentaro Inui. 2021 · 2021
Cited alongside, same era.
Maieutic prompting: Logically consistent reasoning with recursive explanations
Jaehun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, Ronan Le Bras, and Yejin Choi. 2022 · 2022
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Are prompt-based models clueless?
Pride Kavumba, Ryo Takahashi, and Yusuke Oda. 2022 · 2022
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Tell me why! explanations support learning relational and causal structure
Andrew K. Lampinen, Nicholas A. Roy, Ishita Dasgupta, Stephanie Cy Chan, Allison C. Tam, James L. McClelland, Chen Yan, Adam Santoro, Neil C. Rabinowitz, Jane X. Wang, and Felix Hill. 2022 · 2022
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Event transition planning for open-ended text generation
Qintong Li, Piji Li, Wei Bi, Zhaochun Ren, Yuxuan Lai, and Lingpeng Kong. 2022 · 2022
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Holistic evaluation of language models
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel J. Orr, Lucia Zheng, Mert Yüksekgönül, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, and Yuta Koreeda. 2022 · 2022
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Teaching language models to support answers with verified quotes
Jacob Menick, Maja Trebacz, Vladimir Mikulik, John Aslanides, Francis Song, Martin Chadwick, Mia Glaese, Susannah Young, Lucy Campbell-Gillingham, Geoffrey Irving, et al. 2022 · 2022
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Melanie Sclar, Peter West, Sachin Kumar, Yulia Tsvetkov, and Yejin Choi. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed H Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
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Generating sequences by learning to self-correct
Sean Welleck, Ximing Lu, Peter West, Faeze Brahman, Tianxiao Shen, Daniel Khashabi, and Yejin Choi. 2022 · 2022
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Reframing human-ai collaboration for generating free-text explanations
Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, Mark Riedl, and Yejin Choi. 2022 · 2022
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The unreliability of explanations in few-shot prompting for textual reasoning
Xi Ye and Greg Durrett. 2022 · 2022
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Star: Bootstrapping reasoning with reasoning
Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah Goodman. 2022 · 2022
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Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. 2022 · 2022
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