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Language understanding is a multi-faceted cognitive capability, which the Natural Language Processing (NLP) community has striven to model computationally for decades.
Karl Popper: Logik der Forschung
Karl Popper. 1934 · 1934
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David G. Hays. 1979 · 1979
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Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen. 1989 · 1989
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Catastrophic forgetting in connectionist networks
Robert M French. 1999 · 1999
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Linguistic regularities in continuous space word representations
Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig. 2013 · 2013
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The Stanford CoreNLP natural language processing toolkit
Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, and David McClosky. 2014 · 2014
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Fine-grained analysis of sentence embeddings using auxiliary prediction tasks
Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, and Yoav Goldberg. 2017 · 2017
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Two challenges for CI trustworthiness and how to address them
Kevin Baum, Maximilian A. Köhl, and Eva Schmidt. 2017 · 2017
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Distilling a neural network into a soft decision tree
Nicholas Frosst and Geoffrey Hinton. 2017 · 2017
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Limits of end-to-end learning
Tobias Glasmachers. 2017 · 2017
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What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties
Alexis Conneau, German Kruszewski, Guillaume Lample, Loïc Barrault, and Marco Baroni. 2018 · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018 · 2018
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019 · 2019
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Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020 · 2020
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Linguistic profiling of a neural language model
Alessio Miaschi, Dominique Brunato, Felice Dell’Orletta, and Giulia Venturi. 2020 · 2020
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Estimating training data influence by tracing gradient descent
Garima Pruthi, Frederick Liu, Satyen Kale, and Mukund Sundararajan. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
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Beyond accuracy: Behavioral testing of NLP models with CheckList
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020 · 2020
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You can teach an old dog new tricks! on training knowledge graph embeddings
Daniel Ruffinelli, Samuel Broscheit, and Rainer Gemulla. 2020 · 2020
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Making deep neural networks right for the right scientific reasons by interacting with their explanations
Patrick Schramowski, Wolfgang Stammer, Stefano Teso, Anna Brugger, Franziska Herbert, Xiaoting Shao, Hans-Georg Luigs, Anne-Katrin Mahlein, and Kristian Kersting. 2020 · 2020
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No explainability without accountability: An empirical study of explanations and feedback in interactive ml
Alison Smith-Renner, Ron Fan, Melissa Birchfield, Tongshuang Wu, Jordan Boyd-Graber, Daniel S Weld, and Leah Findlater. 2020 · 2020
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Dataset cartography: Mapping and diagnosing datasets with training dynamics
Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith, and Yejin Choi. 2020 · 2020
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Probing pretrained language models for lexical semantics
Ivan Vulić, Edoardo Maria Ponti, Robert Litschko, Goran Glavaš, and Anna Korhonen. 2020 · 2020
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Editing factual knowledge in language models
Nicola De Cao, Wilker Aziz, and Ivan Titov. 2021 · 2021
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Do explanations help users detect errors in open-domain QA? an evaluation of spoken vs. visual explanations
Ana Valeria González, Gagan Bansal, Angela Fan, Yashar Mehdad, Robin Jia, and Srinivasan Iyer. 2021 · 2021
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What makes my model perplexed? a linguistic investigation on neural language models perplexity
Alessio Miaschi, Dominique Brunato, Felice Dell’Orletta, and Giulia Venturi. 2021 · 2021
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Ai and the everything in the whole wide world benchmark
Deborah Raji, Emily Denton, Emily M. Bender, Alex Hanna, and Amandalynne Paullada. 2021 · 2021
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Evaluation examples are not equally informative: How should that change NLP leaderboards?
Pedro Rodriguez, Joe Barrow, Alexander Miserlis Hoyle, John P. Lalor, Robin Jia, and Jordan Boyd-Graber. 2021 · 2021
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That looks hard: Characterizing linguistic complexity in humans and language models
Gabriele Sarti, Dominique Brunato, and Felice Dell’Orletta. 2021 · 2021
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Targeting the benchmark: On methodology in current natural language processing research
David Schlangen. 2021 · 2021
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Composable sparse fine-tuning for cross-lingual transfer
Alan Ansell, Edoardo Ponti, Anna Korhonen, and Ivan Vulić. 2022 · 2022
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Probing in context: Toward building robust classifiers via probing large language models
Afra Amini and Massimiliano Ciaramita. 2023 · 2023
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Faithfulness tests for natural language explanations
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Sparks of artificial general intelligence: Early experiments with gpt-4
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The dangers of trusting stochastic parrots: Faithfulness and trust in open-domain conversational question answering
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How do languages influence each other? studying cross-lingual data sharing during llm fine-tuning
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Caglar Aytekin. 2022 · 2022
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A systematic literature review of user trust in ai-enabled systems: An hci perspective
Tita Alissa Bach, Amna Khan, Harry Hallock, Gabriela Beltrão, and Sonia Sousa. 2022 · 2022
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The dangers of underclaiming: Reasons for caution when reporting how NLP systems fail
Samuel Bowman. 2022 · 2022
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Y. Zhao, Yanping Huang, Andrew M. Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022
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20Q: Overlap-free world knowledge benchmark for language models
Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann, and Walter Daelemans. 2022 · 2022
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On the origin of hallucinations in conversational models: Is it the datasets or the models?
Nouha Dziri, Sivan Milton, Mo Yu, Osmar Zaiane, and Siva Reddy. 2022 · 2022
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Informativeness and invariance: Two perspectives on spurious correlations in natural language
Jacob Eisenstein. 2022 · 2022
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4.2 hmc: A spectrum of human–machine-collaborative relevance judgment frameworks
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When not to trust language models: Investigating effectiveness of parametric and non-parametric memories
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Augmented language models: a survey
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Gpt-4 technical report
R OpenAI. 2023 · 2023
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Art: Automatic multi-step reasoning and tool-use for large language models
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Credible without credit: Domain experts assess generative language models
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Closed ai models make bad baselines
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Toolformer: Language models can teach themselves to use tools
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