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Interpretability and human oversight are fundamental pillars of deploying complex NLP models into real-world applications.
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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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
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Dear sir or madam, may I introduce the GYAFC dataset: Corpus, benchmarks and metrics for formality style transfer
Sudha Rao and Joel Tetreault. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Parameter-efficient transfer learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
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Can you explain that? lucid explanations help human-ai collaborative image retrieval
Arijit Ray, Yi Yao, Rakesh Kumar, Ajay Divakaran, and Giedrius Burachas. 2019 · 2019
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Taking a hint: Leveraging explanations to make vision and language models more grounded
Ramprasaath R Selvaraju, Stefan Lee, Yilin Shen, Hongxia Jin, Shalini Ghosh, Larry Heck, Dhruv Batra, and Devi Parikh. 2019 · 2019
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Do human rationales improve machine explanations?
Julia Strout, Ye Zhang, and Raymond Mooney. 2019 · 2019
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AllenNLP interpret: A framework for explaining predictions of NLP models
Eric Wallace, Jens Tuyls, Junlin Wang, Sanjay Subramanian, Matt Gardner, and Sameer Singh. 2019 · 2019
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Identifying and measuring annotator bias based on annotators’ demographic characteristics
Hala Al Kuwatly, Maximilian Wich, and Georg Groh. 2020 · 2020
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Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, et al. 2020 · 2020
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White paper on artificial intelligence: a european approach to excellence and trust
European Commission. 2020 · 2020
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Explaining black box predictions and unveiling data artifacts through influence functions
Xiaochuang Han, Byron C. Wallace, and Yulia Tsvetkov. 2020 · 2020
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Explanation-based human debugging of NLP models: A survey
Piyawat Lertvittayakumjorn and Francesca Toni. 2021 · 2021
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ExplainaBoard: An explainable leaderboard for NLP
Pengfei Liu, Jinlan Fu, Yang Xiao, Weizhe Yuan, Shuaichen Chang, Junqi Dai, Yixin Liu, Zihuiwen Ye, and Graham Neubig. 2021 · 2021
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Hatexplain: A benchmark dataset for explainable hate speech detection
Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukherjee. 2021 · 2021
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Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI
Robert Munro Monarch. 2021 · 2021
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Putting humans in the natural language processing loop: A survey
Zijie J. Wang, Dongjin Choi, Shenyu Xu, and Diyi Yang. 2021 · 2021
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exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformer Models
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Exploring the limits of transfer learning with a unified text-to-text transformer
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The language interpretability tool: Extensible, interactive visualizations and analysis for NLP models
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Transformers: State-of-the-art natural language processing
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“that is a suspicious reaction!”: Interpreting logits variation to detect NLP adversarial attacks
Edoardo Mosca, Shreyash Agarwal, Javier Rando Ramírez, and Georg Groh. 2022a
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SHAP-based explanation methods: A review for NLP interpretability
Edoardo Mosca, Ferenc Szigeti, Stella Tragianni, Daniel Gallagher, and Georg Groh. 2022b
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BLOOM: A 176b-parameter open-access multilingual language model
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AdapterHub playground: Simple and flexible few-shot learning with adapters
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Natural language processing: State of the art, current trends and challenges
Diksha Khurana, Aditya Koli, Kiran Khatter, and Sukhdev Singh. 2022 · 2022
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Post-hoc interpretability for neural nlp: A survey
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