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A language model's ability to reflect on its own reasoning provides a key advantage for solving complex problems.
Automated Reasoning: Introduction and Applications (2nd edition)
Larry Wos, Ross Overbeek, Rusty Lusk, and Jim Boyle · 1992
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Scaling laws for neural language models, 2020
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2001
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Making monolingual sentence embeddings multilingual using knowledge distillation
Nils Reimers and Iryna Gurevych · 2004
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Pearson correlation coefficient
Israel Cohen, Yiteng Huang, Jingdong Chen, Jacob Benesty, Jacob Benesty, Jingdong Chen, Yiteng Huang, and Israel Cohen · 2009
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2014
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Adversarial attacks on neural network policies
Sandy H. Huang, Nicolas Papernot, Ian J. Goodfellow, Yan Duan, and Pieter Abbeel · 2017
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
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Tal Schuster, Darsh Shah, Yun Jie Serene Yeo, Daniel Roberto Filizzola Ortiz, Enrico Santus, and Regina Barzilay · 2019
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Freelb: Enhanced adversarial training for natural language understanding
Chen Zhu, Yu Cheng, Zhe Gan, S. Sun, Tom Goldstein, and Jingjing Liu · 2019
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SMART: Robust and efficient fine-tuning for pre-trained natural language models through principled regularized optimization
Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Tuo Zhao · 2020
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The limitations of stylometry for detecting machine-generated fake news
Tal Schuster, Roei Schuster, Darsh J. Shah, and Regina Barzilay · 2020
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Extracting training data from large language models
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel · 2021
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Training verifiers to solve math word problems, 2021
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman · 2021
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RLPrompt: Optimizing discrete text prompts with reinforcement learning
Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric Xing, and Zhiting Hu · 2022
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An empirical analysis of compute-optimal large language model training
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Thomas Hennigan, Eric Noland, Katherine Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karén Simonyan, Erich Elsen, Oriol Vinyals, Jack Rae, and Laurent Sifre · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou · 2022
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Can foundation models talk causality?
Moritz Willig, Matej Zečević, Devendra Singh Dhami, and Kristian Kersting · 2022
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
BIG bench authors · 2023
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ChatGPT‘s information seeking strategy: Insights from the 20-questions game
Leonardo Bertolazzi, Davide Mazzaccara, Filippo Merlo, and Raffaella Bernardi · 2023
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Faith and fate: Limits of transformers on compositionality
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Towards a mechanistic interpretation of multi-step reasoning capabilities of language models
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Efficient memory management for large language model serving with pagedattention
Beyond accuracy: Evaluating the reasoning behavior of large language models - a survey
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Assessing logical reasoning capabilities of encoder-only transformer models
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"do anything now": Characterizing and evaluating in-the-wild jailbreak prompts on large language models
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Scaling llm test-time compute optimally can be more effective than scaling model parameters, 2024
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Math-shepherd: Verify and reinforce LLMs step-by-step without human annotations
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Let’s verify step by step, 2023
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Hanmeng Liu, Ruoxi Ning, Zhiyang Teng, Jian Liu, Qiji Zhou, and Yue Zhang · 2023
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Language models are greedy reasoners: A systematic formal analysis of chain-of-thought
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Adversarial attacks and defenses in large language models: Old and new threats
Leo Schwinn, David Dobre, Stephan Günnemann, and Gauthier Gidel · 2023
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Can ChatGPT defend its belief in truth? evaluating LLM reasoning via debate
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An LLM can fool itself: A prompt-based adversarial attack
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