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Chain-of-Thought (CoT) reasoning, which breaks down complex tasks into intermediate reasoning steps, has significantly enhanced the performance of large language models (LLMs) on challenging tasks.
Analysing mathematical reasoning abilities of neural models
David Saxton, Edward Grefenstette, Felix Hill, and Pushmeet Kohli. 2019 · 1904
Earlier work this paper cites.
Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. 2019 · 1909
Earlier work this paper cites.
Perplexity—a measure of the difficulty of speech recognition tasks
Fred Jelinek, Robert L Mercer, Lalit R Bahl, and James K Baker. 1977 · 1977
Earlier work this paper cites.
Language models are few-shot learners
Tom B Brown. 2020 · 2005
Earlier work this paper cites.
Misinformation has high perplexity
Nayeon Lee, Yejin Bang, Andrea Madotto, and Pascale Fung. 2020 · 2006
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
Earlier work this paper cites.
Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. 2021 · 2021
Earlier work this paper cites.
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. 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.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022 · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
Earlier work this paper cites.
Detecting language model attacks with perplexity
Gabriel Alon and Michael Kamfonas. 2023 · 2023
Earlier work this paper cites.
How abilities in large language models are affected by supervised fine-tuning data composition
Guanting Dong, Hongyi Yuan, Keming Lu, Chengpeng Li, Mingfeng Xue, Dayiheng Liu, Wei Wang, Zheng Yuan, Chang Zhou, and Jingren Zhou. 2023 · 2023
Earlier work this paper cites.
Embracing large language models for medical applications: opportunities and challenges
Mert Karabacak and Konstantinos Margetis. 2023 · 2023
Cited alongside, same era.
Improving large language model fine-tuning for solving math problems
Yixin Liu, Avi Singh, C Daniel Freeman, John D Co-Reyes, and Peter J Liu. 2023 · 2023
Cited alongside, same era.
Fine-tuning or retrieval? comparing knowledge injection in llms
Oded Ovadia, Menachem Brief, Moshik Mishaeli, and Oren Elisha. 2023 · 2023
Cited alongside, same era.
Better zero-shot reasoning with self-adaptive prompting
Xingchen Wan, Ruoxi Sun, Hanjun Dai, Sercan O Arik, and Tomas Pfister. 2023 · 2023
Cited alongside, same era.
Metamath: Bootstrap your own mathematical questions for large language models
Orpo: Monolithic preference optimization without reference model
Jiwoo Hong, Noah Lee, and James Thorne. 2024 · 2024
Later among the works it cites.
Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al. 2024 · 2024
Later among the works it cites.
Binary classifier optimization for large language model alignment
Seungjae Jung, Gunsoo Han, Daniel Wontae Nam, and Kyoung-Woon On. 2024 · 2024
Later among the works it cites.
Deductive verification of chain-of-thought reasoning
Zhan Ling, Yunhao Fang, Xuanlin Li, Zhiao Huang, Mingu Lee, Roland Memisevic, and Hao Su. 2024 · 2024
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. 2024 · 2024
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Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu. 2023 · 2023
Cited alongside, same era.
Perplexed by perplexity: Perplexity-based pruning with small reference models
Zachary Ankner, Cody Blakeney, Kartik Sreenivasan, Max Marion, Matthew L Leavitt, and Mansheej Paul · 2024
Cited alongside, same era.
Graph of thoughts: Solving elaborate problems with large language models
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, et al. 2024 · 2024
Cited alongside, same era.
Forest-of-thought: Scaling test-time compute for enhancing llm reasoning
Zhenni Bi, Kai Han, Chuanjian Liu, Yehui Tang, and Yunhe Wang. 2024 · 2024
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Large language monkeys: Scaling inference compute with repeated sampling
Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V Le, Christopher Ré, and Azalia Mirhoseini. 2024 · 2024
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Perplexed: Understanding when large language models are confused
Nathan Cooper and Torsten Scholak. 2024 · 2024
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Few shot chain-of-thought driven reasoning to prompt llms for open ended medical question answering
Ojas Gramopadhye, Saeel Sandeep Nachane, Prateek Chanda, Ganesh Ramakrishnan, Kshitij Sharad Jadhav, Yatin Nandwani, Dinesh Raghu, and Sachindra Joshi. 2024 · 2024
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Aaron Grattafiori and et al. 2024 · 2024
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Archon: An architecture search framework for inference-time techniques
Jon Saad-Falcon, Adrian Gamarra Lafuente, Shlok Natarajan, Nahum Maru, Hristo Todorov, Etash Guha, E Kelly Buchanan, Mayee Chen, Neel Guha, Christopher Ré, et al. 2024 · 2024
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Rethinking data selection for supervised fine-tuning
Ming Shen. 2024 · 2024
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Muse: Machine unlearning six-way evaluation for language models
Weijia Shi, Jaechan Lee, Yangsibo Huang, Sadhika Malladi, Jieyu Zhao, Ari Holtzman, Daogao Liu, Luke Zettlemoyer, Noah A Smith, and Chiyuan Zhang. 2024 · 2024
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. 2024 · 2024
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Qwen Team. 2024 · 2024
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2024 · 2024
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Lima: Less is more for alignment
Chunting Zhou, Pengfei Liu, Puxin Xu, Srinivasan Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, et al. 2024 · 2024
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