Fetching the paper…
Reading the bibliography…
Large Language Models (LLMs) have emerged as powerful support tools across various natural language tasks and a range of application domains.
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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris 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 · 1901
Earlier work this paper cites.
Stop measuring calibration when humans disagree
Joris Baan, Wilker Aziz, Barbara Plank, and Raquel Fernandez. 2022 · 1915
Earlier work this paper cites.
Inherent disagreements in human textual inferences
Ellie Pavlick and Tom Kwiatkowski. 2019 · 2019
Earlier work this paper cites.
What can we learn from collective human opinions on natural language inference data?
Yixin Nie, Xiang Zhou, and Mohit Bansal. 2020 · 2020
Earlier work this paper cites.
Sohail Akhtar, Valerio Basile, and Viviana Patti. 2021 · 2021
Earlier work this paper cites.
ConvAbuse: Data, analysis, and benchmarks for nuanced abuse detection in conversational AI
Amanda Cercas Curry, Gavin Abercrombie, and Verena Rieser. 2021 · 2021
Earlier work this paper cites.
Agreeing to disagree: Annotating offensive language datasets with annotators’ disagreement
Elisa Leonardelli, Stefano Menini, Alessio Palmero Aprosio, Marco Guerini, and Sara Tonelli. 2021 · 2021
Earlier work this paper cites.
On releasing annotator-level labels and information in datasets
Vinodkumar Prabhakaran, Aida Mostafazadeh Davani, and Mark Diaz. 2021 · 2021
Earlier work this paper cites.
Learning from disagreement: A survey
Alexandra N Uma, Tommaso Fornaciari, Dirk Hovy, Silviu Paun, Barbara Plank, and Massimo Poesio. 2021 · 2021
Earlier work this paper cites.
Want To Reduce Labeling Cost? GPT-3 Can Help
Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu, and Michael Zeng. 2021 · 2021
Earlier work this paper cites.
ArMIS - the Arabic misogyny and sexism corpus with annotator subjective disagreements
Dina Almanea and Massimo Poesio. 2022 · 2022
Earlier work this paper cites.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang (Shane) Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
Earlier work this paper cites.
The “problem” of human label variation: On ground truth in data, modeling and evaluation
Barbara Plank. 2022 · 2022
Cited alongside, same era.
Two contrasting data annotation paradigms for subjective NLP tasks
Paul Rottger, Bertie Vidgen, Dirk Hovy, and Janet Pierrehumbert. 2022 · 2022
Cited alongside, same era.
Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V Le. 2022 · 2022
Cited alongside, same era.
Toward a perspectivist turn in ground truthing for predictive computing
Federico Cabitza, Andrea Campagner, and Valerio Basile. 2023 · 2023
Cited alongside, same era.
Is GPT-3 a Good Data Annotator?
Bosheng Ding, Chengwei Qin, Linlin Liu, Yew Ken Chia, Boyang Li, Shafiq Joty, and Lidong Bing. 2023 · 2023
Cited alongside, same era.
Towards measuring the representation of subjective global opinions in language models
Exploring the Sensitivity of LLMs’ Decision-Making Capabilities: Insights from Prompt Variations and Hyperparameters
Manikanta Loya, Divya Sinha, and Richard Futrell. 2023 · 2023
Later among the works it cites.
Are large language models good annotators?
Jay Mohta, Kenan Emir Ak, Yan Xu, and Mingwei Shen. 2023 · 2023
Later among the works it cites.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2023 · 2023
Later among the works it cites.
Whose opinions do language models reflect?
Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto. 2023 · 2023
Later among the works it cites.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al. 2023 · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Esin Durmus, Karina Nyugen, Thomas I. Liao, Nicholas Schiefer, Amanda Askell, Anton Bakhtin, Carol Chen, Zac Hatfield-Dodds, Danny Hernandez, Nicholas Joseph, Liane Lovitt, Sam McCandlish, Orowa Sikder, Alex Tamkin, Janel Thamkul, Jared Kaplan, Jack Clark, and Deep Ganguli. 2023 · 2023
Cited alongside, same era.
ChatGPT outperforms crowd workers for text-annotation tasks
Fabrizio Gilardi, Meysam Alizadeh, and Maël Kubli. 2023 · 2023
Cited alongside, same era.
LLMs Accelerate Annotation for Medical Information Extraction
Akshay Goel, Almog Gueta, Omry Gilon, Chang Liu, Sofia Erell, Lan Huong Nguyen, Xiaohong Hao, Bolous Jaber, Shashir Reddy, Rupesh Kartha, Jean Steiner, Itay Laish, and Amir Feder. 2023 · 2023
Cited alongside, same era.
Annollm: Making large language models to be better crowdsourced annotators
Xingwei He, Zhenghao Lin, Yeyun Gong, Alex Jin, Hang Zhang, Chen Lin, Jian Jiao, Siu Ming Yiu, Nan Duan, Weizhu Chen, et al. 2023 · 2023
Cited alongside, same era.
Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech
Fan Huang, Haewoon Kwak, and Jisun An. 2023 · 2023
Cited alongside, same era.
Can Large Language Models Capture Dissenting Human Voices?
Noah Lee, Na Min An, and James Thorne. 2023 · 2023
Cited alongside, same era.
SemEval-2023 Task 11: Learning with Disagreements (LeWiDi)
Elisa Leonardelli, Gavin Abercrombie, Dina Almanea, Valerio Basile, Tommaso Fornaciari, Barbara Plank, Verena Rieser, Alexandra Uma, and Massimo Poesio. 2023 · 2023
Cited alongside, same era.
Lindia Tjuatja, Valerie Chen, Sherry Tongshuang Wu, Ameet Talwalkar, and Graham Neubig. 2023 · 2023
Later among the works it cites.
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, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
Later among the works it cites.
Petter Törnberg. 2023 · 2023
Later among the works it cites.
LLMaAA: Making Large Language Models as Active Annotators
Ruoyu Zhang, Yanzeng Li, Yongliang Ma, Ming Zhou, and Lei Zou. 2023 · 2023
Later among the works it cites.
Can chatgpt reproduce human-generated labels? a study of social computing tasks
Yiming Zhu, Peixian Zhang, Ehsan-Ul Haq, Pan Hui, and Gareth Tyson. 2023 · 2023
Later among the works it cites.
Quantifying language models’ sensitivity to spurious features in prompt design or: How i learned to start worrying about prompt formatting
Melanie Sclar, Yejin Choi, Yulia Tsvetkov, and Alane Suhr. 2024 · 2024
Closest in time.
Can large language models transform computational social science?
Caleb Ziems, William Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang, and Diyi Yang. 2024 · 2024
Closest in time.