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Recent advancements in large language models have demonstrated remarkable capabilities across various NLP tasks.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 1907
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Portuguese Named Entity Recognition using BERT-CRF
Souza, F.; Nogueira, R. F.; and de Alencar Lotufo, R. 2019 · 1909
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Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition
Tjong Kim Sang, E. F.; and De Meulder, F. 2003 · 2003
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Language Models are Few-Shot Learners
Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D. M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; and Amodei, D. 2020b · 2005
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TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding
Xiao, Z.; Song, W.; Xu, H.; Ren, Z.; and Sun, Y. 2020 · 2006
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Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Shin, T.; Razeghi, Y.; Logan IV, R. L.; Wallace, E.; and Singh, S. 2020 · 2010
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Predicting the political alignment of twitter users
Conover, M. D.; Gonçalves, B.; Ratkiewicz, J.; Flammini, A.; and Menczer, F. 2011 · 2011
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A machine learning approach to twitter user classification
Pennacchiotti, M.; and Popescu, A.-M. 2011 · 2011
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Learning multilingual named entity recognition from Wikipedia
Nothman, J.; Ringland, N.; Radford, W.; Murphy, T.; and Curran, J. R. 2012 · 2012
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Classifying political orientation on Twitter: It’s not easy!
Cohen, R.; and Ruths, D. 2013 · 2013
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Generating Sequences With Recurrent Neural Networks
Graves, A. 2013 · 2013
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Towards Robust Linguistic Analysis using OntoNotes
Pradhan, S.; Moschitti, A.; Xue, N.; Ng, H. T.; Björkelund, A.; Uryupina, O.; Zhang, Y.; and Zhong, Z. 2013 · 2013
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Echo chamber or public sphere? Predicting political orientation and measuring political homophily in Twitter using big data
Colleoni, E.; Rozza, A.; and Arvidsson, A. 2014 · 2014
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Tweeting From Left to Right
Barberá, P.; Jost, J.; Nagler, J.; Tucker, J.; and Bonneau, R. 2015 · 2015
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Ideology detection for twitter users with heterogeneous types of links
Gu, Y.; Chen, T.; Sun, Y.; and Wang, B. 2016 · 2016
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Results of the WNUT2017 Shared Task on Novel and Emerging Entity Recognition
Derczynski, L.; Nichols, E.; van Erp, M.; and Limsopatham, N. 2017 · 2017
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Fake News Detection on Social Media: A Data Mining Perspective
Shu, K.; Sliva, A.; Wang, S.; Tang, J.; and Liu, H. 2017 · 2017
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“Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection
Wang, W. Y. 2017 · 2017
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Analyzing the digital traces of political manipulation: The 2016 russian interference twitter campaign
Badawy, A.; Ferrara, E.; and Lerman, K. 2018 · 2018
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A Survey on Recent Advances in Named Entity Recognition from Deep Learning models
Yadav, V.; and Bethard, S. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Kenton, J. D. M.-W. C.; and Toutanova, L. K. 2019 · 2019
Cited alongside, same era.
Red bots do it better: Comparative analysis of social bot partisan behavior
Luceri, L.; Deb, A.; Badawy, A.; and Ferrara, E. 2019 · 2019
Cited alongside, same era.
Language Models are Unsupervised Multitask Learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; and Sutskever, I. 2019 · 2019
Cited alongside, same era.
How to Fine-Tune BERT for Text Classification?
Sun, C.; Qiu, X.; Xu, Y.; and Huang, X. 2019 · 2019
Cited alongside, same era.
CrossWeigh: Training Named Entity Tagger from Imperfect Annotations
Wang, Z.; Shang, J.; Liu, L.; Lu, L.; Liu, J.; and Han, J. 2019 · 2019
Cited alongside, same era.
Experiment Tracking with Weights and Biases
Biewald, L. 2020 · 2020
Training language models to follow instructions with human feedback
Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Ray, A.; et al. 2022 · 2022
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URJC-Team at PoliticEs 2022: Political Ideology Prediction using Linear Classifiers
Rodríguez-García, M. Á.; Herranz, S. M.; and Unanue, R. M. 2022 · 2022
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Detecting and mitigating the dissemination of fake news: Challenges and future research opportunities
Shahid, W.; Jamshidi, B.; Hakak, S.; Isah, H.; Khan, W. Z.; Khan, M. K.; and Choo, K.-K. R. 2022 · 2022
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Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognition
Su, J.; Murtadha, A.; Pan, S.; Hou, J.; Sun, J.; Huang, W.; Wen, B.; and Liu, Y. 2022 · 2022
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Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Ichter, B.; Xia, F.; Chi, E.; Le, Q.; and Zhou, D. 2022 · 2022
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Cited alongside, same era.
Partisan public health: how does political ideology influence support for COVID-19 related misinformation?
Havey, N. F. 2020 · 2020
Cited alongside, same era.
Predicting the topical stance and political leaning of media using tweets
Stefanov, P.; Darwish, K.; Atanasov, A.; and Nakov, P. 2020 · 2020
Cited alongside, same era.
Application of pre-training models in named entity recognition
Wang, Y.; Sun, Y.; Ma, Z.; Gao, L.; Xu, Y.; and Sun, T. 2020 · 2020
Cited alongside, same era.
LoRA: Low-Rank Adaptation of Large Language Models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2021 · 2021
Cited alongside, same era.
Social media polarization and echo chambers in the context of COVID-19: Case study
Jiang, J.; Ren, X.; Ferrara, E.; et al. 2021 · 2021
Cited alongside, same era.
FakeBERT: Fake News Detection in Social Media with a BERT-Based Deep Learning Approach
Kaliyar, R. K.; Goswami, A.; and Narang, P. 2021 · 2021
Cited alongside, same era.
Most users do not follow political elites on Twitter; those who do show overwhelming preferences for ideological congruity
Wojcieszak, M.; Casas, A.; Yu, X.; Nagler, J.; and Tucker, J. A. 2022 · 2022
Later among the works it cites.
LLM-Leaderboard
2023 · 2023
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How is ChatGPT’s behavior changing over time?
Chen, L.; Zaharia, M.; and Zou, J. 2023 · 2023
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QLoRA: Efficient Finetuning of Quantized LLMs
Dettmers, T.; Pagnoni, A.; Holtzman, A.; and Zettlemoyer, L. 2023 · 2023
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LLaMA Efficient Tuning
hiyouga. 2023 · 2023
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Li, X.; Zhu, X.; Ma, Z.; Liu, X.; and Shah, S. 2023 · 2023
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Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models
Liu, Y.; Han, T.; Ma, S.; Zhang, J.; Yang, Y.; Tian, J.; He, H.; Li, A.; He, M.; Liu, Z.; Wu, Z.; Zhu, D.; Li, X.; Qiang, N.; Shen, D.; Liu, T.; and Ge, B. 2023 · 2023
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OpenAI. 2023 · 2023
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Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4
Pelrine, K.; Reksoprodjo, M.; Gupta, C.; Christoph, J.; and Rabbany, R. 2023 · 2023
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On the Challenges of Using Black-Box APIs for Toxicity Evaluation in Research
Pozzobon, L.; Ermis, B.; Lewis, P.; and Hooker, S. 2023 · 2023
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Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Rafailov, R.; Sharma, A.; Mitchell, E.; Ermon, S.; Manning, C. D.; and Finn, C. 2023 · 2023
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Törnberg, P. 2023 · 2023
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Gpt-ner: Named entity recognition via large language models
Wang, S.; Sun, X.; Li, X.; Ouyang, R.; Wu, F.; Zhang, T.; Li, J.; and Wang, G. 2023 · 2023
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Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Yao, S.; Yu, D.; Zhao, J.; Shafran, I.; Griffiths, T. L.; Cao, Y.; and Narasimhan, K. 2023 · 2023
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A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models
Ye, J.; Chen, X.; Xu, N.; Zu, C.; Shao, Z.; Liu, S.; Cui, Y.; Zhou, Z.; Gong, C.; Shen, Y.; Zhou, J.; Chen, S.; Gui, T.; Zhang, Q.; and Huang, X. 2023 · 2023
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