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The emergence of large language models (LLMs) has revolutionized numerous applications across industries.
A new readability yardstick
Rudolph Flesch · 1948
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A value for n-person games
Lloyd S Shapley et al · 1953
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Random forests
Leo Breiman · 2001
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A survey of the state of explainable AI for natural language processing
Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, and Prithviraj Sen · 2010
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Training and analyzing deep recurrent neural networks
Michiel Hermans and Benjamin Schrauwen · 2013
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Measuring the similarity between automatically generated topics
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Visualizing and understanding recurrent networks, 2015
Andrej Karpathy, Justin Johnson, and Li Fei-Fei · 2015
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A Unified Approach to Interpreting Model Predictions , volume 30
Scott M Lundberg and Su-In Lee · 2017
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Attention is all you need
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Explainable artificial intelligence: A survey
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Know what you don’t know: Unanswerable questions for squad, 2018
Pranav Rajpurkar, Robin Jia, and Percy Liang · 2018
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What does BERT look at? an analysis of BERT’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning · 2019
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Do attention heads in bert track syntactic dependencies?, 2019
Phu Mon Htut, Jason Phang, Shikha Bordia, and Samuel R. Bowman · 2019
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Attention is not explanation, 2019
Sarthak Jain and Byron C. Wallace · 2019
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Probing what different nlp tasks teach machines about function word comprehension, 2019
Najoung Kim, Roma Patel, Adam Poliak, Alex Wang, Patrick Xia, R. Thomas McCoy, Ian Tenney, Alexis Ross, Tal Linzen, Benjamin Van Durme, Samuel R. Bowman, and Ellie Pavlick · 2019
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Revealing the dark secrets of bert, 2019
Olga Kovaleva, Alexey Romanov, Anna Rogers, and Anna Rumshisky · 2019
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Linguistic knowledge and transferability of contextual representations, 2019
Nelson F. Liu, Matt Gardner, Yonatan Belinkov, Matthew E. Peters, and Noah A. Smith · 2019
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Is attention interpretable?, 2019
Sofia Serrano and Noah A. Smith · 2019
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What do you learn from context? probing for sentence structure in contextualized word representations, 2019
Perturbed masking: Parameter-free probing for analyzing and interpreting bert
Zhiyong Wu, Yun Chen, Ben Kao, and Qun Liu · 2020
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Probing classifiers: Promises, shortcomings, and advances, 2021
Yonatan Belinkov · 2021
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On the dangers of stochastic parrots: Can language models be too big?
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
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Explainability for natural language processing
Marina Danilevsky, Shipi Dhanorkar, Yunyao Li, Lucian Popa, Kun Qian, and Anbang Xu · 2021
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Explainable AI: current status and future directions
Prashant Gohel, Priyanka Singh, and Manoranjan Mohanty · 2021
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Linguistic dependencies and statistical dependence
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Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, R Thomas McCoy, Najoung Kim, Benjamin Van Durme, Samuel R. Bowman, Dipanjan Das, and Ellie Pavlick · 2019
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Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter · 2019
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Explainable ai: A brief survey on history, research areas, approaches and challenges
Feiyu Xu, Hans Uszkoreit, Yangzhou Du, Wei Fan, Dongyan Zhao, and Jun Zhu · 2019
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A survey of the state of explainable AI for natural language processing
Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, and Prithviraj Sen · 2020
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What bert is not: Lessons from a new suite of psycholinguistic diagnostics for language models, 2020
Allyson Ettinger · 2020
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Human trust in artificial intelligence: Review of empirical research
Ella Glikson and Anita Williams Woolley · 2020
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A primer in bertology: What we know about how bert works, 2020
Anna Rogers, Olga Kovaleva, and Anna Rumshisky · 2020
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Jacob Louis Hoover, Wenyu Du, Alessandro Sordoni, and Timothy J. O’Donnell · 2021
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Interpreting deep learning models in natural language processing: A review, 2021
Xiaofei Sun, Diyi Yang, Xiaoya Li, Tianwei Zhang, Yuxian Meng, Han Qiu, Guoyin Wang, Eduard Hovy, and Jiwei Li · 2021
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Universal adversarial triggers for attacking and analyzing nlp, 2021
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh · 2021
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Analyzing how bert performs entity matching
Matteo Paganelli, Francesco Del Buono, Andrea Baraldi, and Francesco Guerra · 2022
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Bias and fairness in large language models: A survey, 2023
Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, Md Mehrab Tanjim, Sungchul Kim, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, and Nesreen K. Ahmed · 2023
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A survey on fairness in large language models, 2023
Yingji Li, Mengnan Du, Rui Song, Xin Wang, and Ying Wang · 2023
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Did you read the instructions? rethinking the effectiveness of task definitions in instruction learning, 2023
Fan Yin, Jesse Vig, Philippe Laban, Shafiq Joty, Caiming Xiong, and Chien-Sheng Jason Wu · 2023
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