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Many reasoning, planning, and problem-solving tasks share an intrinsic algorithmic nature: correctly simulating each step is a sufficient condition to solve them correctly.
Minds, brains, and programs
John R Searle · 1980
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Machine theory of mind
Neil Rabinowitz, Frank Perbet, Francis Song, Chiyuan Zhang, S. M. Ali Eslami, and Matthew Botvinick · 2018
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, et al · 2020
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The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al · 2020
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Jurassic-1: Technical details and evaluation
Opher Lieber, Or Sharir, Barak Lenz, and Yoav Shoham · 2021
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Show your work: Scratchpads for intermediate computation with language models
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al · 2021
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Attention is turing complete
Jorge Pérez, Pablo Barceló, and Javier Marinkovic · 2021
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Thinking like transformers
Gail Weiss, Yoav Goldberg, and Eran Yahav · 2021
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Position information in transformers: An overview
Philipp Dufter, Martin Schmitt, and Hinrich Schütze · 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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Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, et al · 2022
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In-context learning and induction heads
Catherine Olsson, Nelson Elhage, Neel Nanda, et al · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, et al · 2022
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BLOOM: A 176B-parameter open-access multilingual language model
Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al · 2022
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Talking about large language models
Murray Shanahan · 2022
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Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models
Priyan Vaithilingam, Tianyi Zhang, and Elena L. Glassman · 2022
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Statistically meaningful approximation: a case study on approximating turing machines with transformers
Colin Wei, Yining Chen, and Tengyu Ma · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou · 2022
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The reversal curse: Llms trained on" a is b" fail to learn" b is a"
Lukas Berglund, Meg Tong, Max Kaufmann, Mikita Balesni, Asa Cooper Stickland, Tomasz Korbak, and Owain Evans · 2023
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Emergent and predictable memorization in large language models
Stella Biderman, USVSN Sai Prashanth, Lintang Sutawika, Hailey Schoelkopf, Quentin Anthony, Shivanshu Purohit, and Edward Raf · 2023
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Faith and fate: Limits of transformers on compositionality
Nouha Dziri, Ximing Lu, Melanie Sclar, Xiang Lorraine Li, Liwei Jian, Bill Yuchen Lin, Peter West, Chandra Bhagavatula, Ronan Le Bras, Jena D Hwang, et al · 2023
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Who’s harry potter? approximate unlearning in llms
Ronen Eldan and Mark Russinovich · 2023
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Mathematical capabilities of ChatGPT
Simon Frieder, Luca Pinchetti, Alexis Chevalier, Ryan-Rhys Griffiths, Tommaso Salvatori, Thomas Lukasiewicz, Philipp Christian Petersen, and Julius Berner · 2023
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Looped transformers as programmable computers
Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D Lee, and Dimitris Papailiopoulos · 2023
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LLaMA 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Predicting code coverage without execution
Michele Tufano, Shubham Chandel, Anisha Agarwal, Neel Sundaresan, and Colin Clement · 2023
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Self-consistency improves chain of thought reasoning in language models, 2023
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2023
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Emergent analogical reasoning in large language models, 2023
Taylor Webb, Keith J. Holyoak, and Hongjing Lu · 2023
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The generative ai paradox:" what it can create, it may not understand"
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Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, and Zhiting Hu · 2023
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Large language models for software engineering: A systematic literature review
Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, and Haoyu Wang · 2023
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Gpt is becoming a turing machine: Here are some ways to program it
Ana Jojic, Zhen Wang, and Nebojsa Jojic · 2023
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The arrt of language-models-as-a-service: Overview of a new paradigm and its challenges
Emanuele La Malfa, Aleksandar Petrov, Simon Frieder, Christoph Weinhuber, Ryan Burnell, Anthony G Cohn, Nigel Shadbolt, and Michael Wooldridge · 2023
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Code as policies: Language model programs for embodied control
Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, and Andy Zeng · 2023
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Code execution with pre-trained language models
Chenxiao Liu, Shuai Lu, Weizhu Chen, Daxin Jiang, Alexey Svyatkovskiy, Shengyu Fu, Neel Sundaresan, and Nan Duan · 2023
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How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN
R Thomas McCoy, Paul Smolensky, Tal Linzen, Jianfeng Gao, and Asli Celikyilmaz · 2023
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R Thomas McCoy, Shunyu Yao, Dan Friedman, Matthew Hardy, and Thomas L Griffiths · 2023
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Peter West, Ximing Lu, Nouha Dziri, Faeze Brahman, Linjie Li, Jena D Hwang, Liwei Jiang, Jillian Fisher, Abhilasha Ravichander, Khyathi Chandu, et al · 2023
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Addressing compiler errors: Stack overflow or large language models?
Patricia Widjojo and Christoph Treude · 2023
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What do code models memorize? an empirical study on large language models of code
Zhou Yang, Zhipeng Zhao, Chenyu Wang, Jieke Shi, Dongsun Kim, DongGyun Han, and David Lo · 2023
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How well do large language models perform in arithmetic tasks?
Zheng Yuan, Hongyi Yuan, Chuanqi Tan, Wei Wang, and Songfang Huang · 2023
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Large language models meet nl2code: A survey
Daoguang Zan, Bei Chen, Fengji Zhang, Dianjie Lu, Bingchao Wu, Bei Guan, Wang Yongji, and Jian-Guang Lou · 2023
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Can transformers learn to solve problems recursively?
Shizhuo Dylan Zhang, Curt Tigges, Stella Biderman, Maxim Raginsky, and Talia Ringer · 2023
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What algorithms can transformers learn? a study in length generalization
Hattie Zhou, Arwen Bradley, Etai Littwin, Noam Razin, Omid Saremi, Josh Susskind, Samy Bengio, and Preetum Nakkiran · 2023
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Llama 3 model card
AI@Meta · 2024
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Evaluating large language models with runtime behavior of program execution, 2024
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Can language models pretend solvers? logic code simulation with llms, 2024
Minyu Chen, Guoqiang Li, Ling-I Wu, Ruibang Liu, Yuxin Su, Xi Chang, and Jianxin Xue · 2024
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Cruxeval: A benchmark for code reasoning, understanding and execution, 2024
Alex Gu, Baptiste Rozière, Hugh Leather, Armando Solar-Lezama, Gabriel Synnaeve, and Sida I. Wang · 2024
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Graph-enhanced large language models in asynchronous plan reasoning, 2024
Fangru Lin, Emanuele La Malfa, Valentin Hofmann, Elle Michelle Yang, Anthony Cohn, and Janet B. Pierrehumbert · 2024
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Codemind: A framework to challenge large language models for code reasoning, 2024
Changshu Liu, Shizhuo Dylan Zhang, Ali Reza Ibrahimzada, and Reyhaneh Jabbarvand · 2024
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Language models don’t always say what they think: unfaithful explanations in chain-of-thought prompting
Miles Turpin, Julian Michael, Ethan Perez, and Samuel Bowman · 2024
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Transformer-based models are not yet perfect at learning to emulate structural recursion, 2024
Dylan Zhang, Curt Tigges, Zory Zhang, Stella Biderman, Maxim Raginsky, and Talia Ringer · 2024
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