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For simple classification tasks, we show that users can benefit from the advantages of using small, local, generative language models instead of large commercial models without a trade-off in performance or introducing extra labelling costs.
Two-sided confidence intervals for the single proportion: comparison of seven methods
Robert G Newcombe · 1998
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Uci machine learning repository
Andrew Frank · 2010
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Good debt or bad debt: Detecting semantic orientations in economic texts
P. Malo, A. Sinha, P. Korhonen, J. Wallenius, and P. Takala · 2014
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee · 2014
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Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2016
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Stop clickbait: Detecting and preventing clickbaits in online news media
Abhijnan Chakraborty, Bhargavi Paranjape, Sourya Kakarla, and Niloy Ganguly · 2016
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SemEval-2016 task 6: Detecting stance in tweets
Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry · 2016
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The (home) bias of european central bankers: new evidence based on speeches
Hamza Bennani and Matthias Neuenkirch · 2017
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Words are not all created equal: A new measure of ecb communication
Matthieu Picault and Thomas Renault · 2017
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Extended comparisons of best subset selection, forward stepwise selection, and the lasso
Trevor Hastie, Robert Tibshirani, and Ryan J Tibshirani · 2017
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Semeval-2018 task 1: Affect in tweets
Saif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, and Svetlana Kiritchenko · 2018
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Semeval-2018 task 3: Irony detection in english tweets
Cynthia Van Hee, Els Lefever, and Véronique Hoste · 2018
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What does bert learn about the structure of language?
Ganesh Jawahar, Benoît Sagot, and Djamé Seddah · 2019
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Semeval-2019 task 6: Identifying and categorizing offensive language in social media (offenseval)
Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, and Ritesh Kumar · 2019
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Semeval-2019 task 5: Multilingual detection of hate speech against immigrants and women in twitter
Valerio Basile, Cristina Bosco, Elisabetta Fersini, Debora Nozza, Viviana Patti, Francisco Manuel Rangel Pardo, Paolo Rosso, and Manuela Sanguinetti · 2019
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Language models are few-shot learners, 2020
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
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Stanza: A Python natural language processing toolkit for many human languages
Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, and Christopher D. Manning · 2020
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Tweeteval: Unified benchmark and comparative evaluation for tweet classification
Francesco Barbieri, Jose Camacho-Collados, Leonardo Neves, and Luis Espinosa-Anke · 2020
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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Fednlp: an interpretable nlp system to decode federal reserve communications
Jean Lee, Hoyoul Luis Youn, Nicholas Stevens, Josiah Poon, and Soyeon Caren Han · 2021
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Impact of news on the commodity market: Dataset and results
Ankur Sinha and Tanmay Khandait · 2021
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Probing classifiers: Promises, shortcomings, and advances
Yonatan Belinkov · 2022
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Shoring up the foundations: Fusing model embeddings and weak supervision
Mayee F Chen, Daniel Y Fu, Dyah Adila, Michael Zhang, Frederic Sala, Kayvon Fatahalian, and Christopher Ré · 2022
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Central bank mandates and monetary policy stances: Through the lens of federal reserve speeches
Christoph Bertsch, Isaiah Hull, Robin L Lumsdaine, and Xin Zhang · 2022
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Making text count: economic forecasting using newspaper text
Eleni Kalamara, Arthur Turrell, Chris Redl, George Kapetanios, and Sujit Kapadia · 2022
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News media versus FRED-MD for macroeconomic forecasting
Jon Ellingsen, Vegard H Larsen, and Leif Anders Thorsrud · 2022
SpQR: A sparse-quantized representation for near-lossless LLM weight compression, 2023
Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, and Dan Alistarh · 2023
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Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
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Rwkv: Reinventing rnns for the transformer era
Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Huanqi Cao, Xin Cheng, Michael Chung, Matteo Grella, Kranthi Kiran GV, et al · 2023
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Fly-swat or cannon? cost-effective language model choice via meta-modeling
Marija Šakota, Maxime Peyrard, and Robert West · 2023
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Explainability for large language models: A survey, 2023
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Cited alongside, same era.
Mteb: Massive text embedding benchmark
Niklas Muennighoff, Nouamane Tazi, Loïc Magne, and Nils Reimers · 2022
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
Gemini Team, Google · 2023
Cited alongside, same era.
Xianzhi Li, Xiaodan Zhu, Zhiqiang Ma, Xiaomo Liu, and Sameena Shah · 2023
Cited alongside, same era.
Llama 2: Open foundation and fine-tuned chat models, 2023
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom · 2023
Cited alongside, same era.
GPT is an effective tool for multilingual psychological text analysis
Steve Rathje, Dan-Mircea Mirea, Ilia Sucholutsky, Raja Marjieh, Claire Robertson, and Jay J Van Bavel · 2023
Cited alongside, same era.
Can GPT-4 support analysis of textual data in tasks requiring highly specialized domain expertise?
Jaromir Savelka, Kevin D Ashley, Morgan A Gray, Hannes Westermann, and Huihui Xu · 2023
Cited alongside, same era.
Haiyan Zhao, Hanjie Chen, Fan Yang, Ninghao Liu, Huiqi Deng, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, and Mengnan Du · 2023
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Shortcut learning of large language models in natural language understanding, 2023
Mengnan Du, Fengxiang He, Na Zou, Dacheng Tao, and Xia Hu · 2023
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Centralbankroberta: A fine-tuned large language model for central bank communications
Moritz Pfeifer and Vincent P Marohl · 2023
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C-pack: Packaged resources to advance general chinese embedding, 2023
Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighoff · 2023
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Trillion dollar words: A new financial dataset, task & market analysis
Agam Shah, Suvan Paturi, and Sudheer Chava · 2023
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Gpt-4 technical report, 2024
OpenAI · 2024
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The claude 3 model family: Opus, sonnet, haiku
AI Anthropic · 2024
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Ai on ai: Exploring the utility of gpt as an expert annotator of ai publications
Autumn Toney-Wails, Christian Schoeberl, and James Dunham · 2024
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Language models understand numbers, at least partially
Fangwei Zhu, Damai Dai, and Zhifang Sui · 2024
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Simple techniques for enhancing sentence embeddings in generative language models
Bowen Zhang, Kehua Chang, and Chunping Li · 2024
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Enhancing in-context learning via linear probe calibration
Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz, Theodoros Salonidis, and Tianyi Chen · 2024
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Embroid: Unsupervised prediction smoothing can improve few-shot classification
Neel Guha, Mayee Chen, Kush Bhatia, Azalia Mirhoseini, Frederic Sala, and Christopher Ré · 2024
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Dora: Weight-decomposed low-rank adaptation
Shih-Yang Liu, Chien-Yi Wang, Hongxu Yin, Pavlo Molchanov, Yu-Chiang Frank Wang, Kwang-Ting Cheng, and Min-Hung Chen · 2024
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The era of 1-bit llms: All large language models are in 1.58 bits
Shuming Ma, Hongyu Wang, Lingxiao Ma, Lei Wang, Wenhui Wang, Shaohan Huang, Li Dong, Ruiping Wang, Jilong Xue, and Furu Wei · 2024
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EU AI Act
EU AI Act · 2024
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