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Thinking Tokens (TT) have been proposed as an unsupervised method to facilitate reasoning in language models.
Thinking tokens for language modeling
David Herel and Tomas Mikolov. 2023 · 2023
Earlier work this paper cites.
Thoughtsource: A central hub for large language model reasoning data
Simon Ott, Konstantin Hebenstreit, Valentin Liévin, Christoffer Egeberg Hother, Milad Moradi, Maximilian Mayrhauser, Robert Praas, Ole Winther, and Matthias Samwald. 2023 · 2023
Earlier work this paper cites.
Towards revealing the mystery behind chain of thought: A theoretical perspective
Xue Zhang, Yuchen Sun, et al. 2023 · 2023
Cited alongside, same era.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024 · 2024
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou. 2022a
Cited in the paper.
Chain of thought prompting elicits reasoning in large language models
Jason Wei et al. 2022b
Cited in the paper.
Think before you speak: Training language models with pause tokens
Sachin Goyal, Ziwei Ji, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar, and Vaishnavh Nagarajan. 2024 · 2024
Closest in time.
Auto-regressive next-token predictors are universal learners
Eran Malach et al. 2024 · 2024
Closest in time.
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