Fetching the paper…
Reading the bibliography…
Recently large language models (LLMs) like ChatGPT have shown impressive performance on many natural language processing tasks with zero-shot.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
Earlier work this paper cites.
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. 2019 · 1907
Earlier work this paper cites.
Finbert: Financial sentiment analysis with pre-trained language models
Dogu Araci. 2019 · 1908
Earlier work this paper cites.
Quest for central bank communication: Does it pay to be “talkative”?
Marek Rozkrut, Krzysztof Rybiński, Lucyna Sztaba, and Radosław Szwaja. 2007 · 2007
Earlier work this paper cites.
When is a liability not a liability? textual analysis, dictionaries, and 10-ks
Tim Loughran and Bill McDonald. 2011 · 2011
Earlier work this paper cites.
Good debt or bad debt: Detecting semantic orientations in economic texts
Pekka Malo, Ankur Sinha, Pekka Korhonen, Jyrki Wallenius, and Pyry Takala. 2014 · 2014
Earlier work this paper cites.
Between hawks and doves: measuring central bank communication
Ellen Tobback, Stefano Nardelli, and David Martens. 2017 · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
Transparency and deliberation within the fomc: a computational linguistics approach
Stephen Hansen, Michael McMahon, and Andrea Prat. 2018 · 2018
Earlier work this paper cites.
Stock returns over the fomc cycle
Anna Cieslak, Adair Morse, and Annette Vissing-Jorgensen. 2019 · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
Cited alongside, same era.
Does central bank communication signal future monetary policy in a (post)-crisis era? the case of the ecb
Hamza Bennani, Nicolas Fanta, Pavel Gertler, and Roman Horvath. 2020 · 2020
Cited alongside, same era.
The tone of the beige book and the pre-fomc announcement drift
Yasutomo Tsukioka and Takahiro Yamasaki. 2020 · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 2020
Augmented language models: a survey
Grégoire Mialon, Roberto Dessì, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, et al. 2023 · 2023
Closest in time.
Chatgpt survey: Performance on nlp datasets
Matúš Pikuliak. 2023 · 2023
Closest in time.
Is chatgpt a general-purpose natural language processing task solver?
Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang. 2023 · 2023
Closest in time.
Closed ai models make bad baselines
Anna Rogers, Niranjan Balasubramanian, Leon Derczynski, Jesse Dodge, Alexander Koller, Sasha Luccioni, Maarten Sap, Roy Schwartz, Noah A. Smith, and Emma Strubell. 2023 · 2023
Closest in time.
The influence of chatgpt on artificial intelligence related crypto assets: Evidence from a synthetic control analysis
Aman Saggu and Lennart Ante. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Databricks’ dolly-v2-12b, an instruction-following large language model
Databricks. 2023 · 2023
Cited alongside, same era.
Pythia scaling suite
EleutherAI. 2023 · 2023
Cited alongside, same era.
The colour of finance words
Diego Garcia, Xiaowen Hu, and Maximilian Rohrer. 2023 · 2023
Cited alongside, same era.
Can chatgpt decipher fedspeak?
Anne Lundgaard Hansen and Sophia Kazinnik. 2023 · 2023
Cited alongside, same era.
H2o.ai’s h2ogpt-oasst1-512-12b, a 12 billion parameter instruction-following large language model
H2O.ai. 2023a
Cited in the paper.
H2o.ai’s openassistant_oasst1_h2ogpt_graded, an open-source instruct-type dataset for fine-tuning of large language models
H2O.ai. 2023b
Cited in the paper.
Trillion dollar words: A new financial dataset, task & market analysis
Agam Shah, Suvan Paturi, and Sudheer Chava. 2023a
Cited in the paper.
Closest in time.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023
Closest in time.
Bloomberggpt: A large language model for finance
Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, and Gideon Mann. 2023 · 2023
Closest in time.
A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023 · 2023
Closest in time.