Fetching the paper…
Reading the bibliography…
Work done to uncover the knowledge encoded within pre-trained language models rely on annotated corpora or human-in-the-loop methods.
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. 2019b · 1907
Earlier work this paper cites.
ALBERT: a lite BERT for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019 · 1909
Earlier work this paper cites.
The measurement of observer agreement for categorical data
J Richard Landis and Gary G Koch. 1977 · 1977
Earlier work this paper cites.
Agglomerative clustering using the concept of mutual nearest neighbourhood
K Chidananda Gowda and G Krishna. 1978 · 1978
Earlier work this paper cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared 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 M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher 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 · 2005
Earlier work this paper cites.
Statistical methods for rates and proportions
Joseph L Fleiss, Bruce Levin, and Myunghee Cho Paik. 2013 · 2013
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Diederik Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Visualizing and understanding recurrent networks
Andrej Karpathy, Justin Johnson, and Li Fei-Fei. 2015 · 2015
Earlier work this paper cites.
Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies
Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg. 2016 · 2016
Earlier work this paper cites.
Investigating Language Universal and Specific Properties in Word Embeddings
Peng Qian, Xipeng Qiu, and Xuanjing Huang. 2016 · 2016
Earlier work this paper cites.
Representation of linguistic form and function in recurrent neural networks
Akos Kádár, Grzegorz Chrupała, and Afra Alishahi. 2017 · 2017
Earlier work this paper cites.
Word representation models for morphologically rich languages in neural machine translation
Ekaterina Vylomova, Trevor Cohn, Xuanli He, and Gholamreza Haffari. 2017 · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Identifying and controlling important neurons in neural machine translation
Anthony Bau, Yonatan Belinkov, Hassan Sajjad, Nadir Durrani, Fahim Dalvi, and James Glass. 2019 · 2019
Earlier work this paper cites.
What is one grain of sand in the desert? analyzing individual neurons in deep nlp models
Fahim Dalvi, Nadir Durrani, Hassan Sajjad, Yonatan Belinkov, D. Anthony Bau, and James Glass. 2019 · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Designing and interpreting probes with control tasks
John Hewitt and Percy Liang. 2019 · 2019
Cited alongside, same era.
Linguistic knowledge and transferability of contextual representations
Nelson F. Liu, Matt Gardner, Yonatan Belinkov, Matthew E. Peters, and Noah A. Smith. 2019a · 2019
Cited alongside, same era.
BERT rediscovers the classical NLP pipeline
Ian Tenney, Dipanjan Das, and Ellie Pavlick. 2019 · 2019
Cited alongside, same era.
Xlnet: Generalized autoregressive pretraining for language understanding
On the pitfalls of analyzing individual neurons in language models
Omer Antverg and Yonatan Belinkov. 2022 · 2022
Later among the works it cites.
Discovering latent concepts learned in BERT
Fahim Dalvi, Abdul Rafae Khan, Firoj Alam, Nadir Durrani, Jia Xu, and Hassan Sajjad. 2022 · 2022
Later among the works it cites.
Is gpt-3 a good data annotator?
Bosheng Ding, Chengwei Qin, Linlin Liu, Lidong Bing, Shafiq Joty, and Boyang Li. 2022 · 2022
Later among the works it cites.
Linguistic correlation analysis: Discovering salient neurons in deepnlp models
Nadir Durrani, Fahim Dalvi, and Hassan Sajjad. 2022 · 2022
Later among the works it cites.
Latent topology induction for understanding contextualized representations
Yao Fu and Mirella Lapata. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
Cited alongside, same era.
On the linguistic representational power of neural machine translation models
Yonatan Belinkov, Nadir Durrani, Fahim Dalvi, Hassan Sajjad, and James Glass. 2020 · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Intrinsic probing through dimension selection
Lucas Torroba Hennigen, Adina Williams, and Ryan Cotterell. 2020 · 2020
Cited alongside, same era.
Asking without telling: Exploring latent ontologies in contextual representations
Julian Michael, Jan A. Botha, and Ian Tenney. 2020 · 2020
Cited alongside, same era.
How transfer learning impacts linguistic knowledge in deep nlp models?
Nadir Durrani, Hassan Sajjad, and Fahim Dalvi. 2021 · 2021
Cited alongside, same era.
Transformer feed-forward layers are key-value memories
Mor Geva, Roei Schuster, Jonathan Berant, and Omer Levy. 2021 · 2021
Cited alongside, same era.
Katharina Jeblick, Balthasar Schachtner, Jakob Dexl, Andreas Mittermeier, Anna Theresa Stüber, Johanna Topalis, Tobias Weber, Philipp Wesp, Bastian Sabel, Jens Ricke, and Michael Ingrisch. 2022 · 2022
Later among the works it cites.
Analyzing encoded concepts in transformer language models
Hassan Sajjad, Nadir Durrani, Fahim Dalvi, Firoj Alam, Abdul Rafae Khan, and Jia Xu. 2022b · 2022
Later among the works it cites.
Benchmarking arabic ai with large language models
Ahmed Abdelali, Hamdy Mubarak, Shammur Absar Chowdhury, Maram Hasanain, Basel Mousi, Sabri Boughorbel, Yassine El Kheir, Daniel Izham, Fahim Dalvi, Majd Hawasly, Nizi Nazar, Yousseif Elshahawy, Ahmed Ali, Nadir Durrani, Natasa Milic-Frayling, and Firoj Alam. 2023 · 2023
Closest in time.
Conceptx: A framework for latent concept analysis
Firoj Alam, Fahim Dalvi, Nadir Durrani, Hassan Sajjad, Khan, Abdul Rafae, and Jia Xu. 2023 · 2023
Closest in time.
Chatgpt outperforms crowd-workers for text-annotation tasks
Fabrizio Gilardi, Meysam Alizadeh, and Maël Kubli. 2023 · 2023
Closest in time.
How close is chatgpt to human experts? comparison corpus, evaluation, and detection
Biyang Guo, Xin Zhang, Ziyuan Wang, Minqi Jiang, Jinran Nie, Yuxuan Ding, Jianwei Yue, and Yupeng Wu. 2023 · 2023
Closest in time.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2023 · 2023
Closest in time.
Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2023 · 2023
Closest in time.
How would stance detection techniques evolve after the launch of chatgpt?
Bowen Zhang, Daijun Ding, and Liwen Jing. 2023 · 2023
Closest in time.