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Large Language Models have shown tremendous performance on a large variety of natural language processing tasks, ranging from text comprehension to common sense reasoning.
“RoBERTa: A Robustly Optimized BERT Pretraining Approach”
Yinhan Liu et al · 1907
Earlier work this paper cites.
“On the Measure of Intelligence”
François Chollet · 1911
Earlier work this paper cites.
“Raven standard progressive matrices”
John Raven · 1938
Earlier work this paper cites.
“What one intelligence test measures: a theoretical account of the processing in the Raven Progressive Matrices Test.”
Patricia Carpenter, Marcel Just and Peter Shell · 1990
Earlier work this paper cites.
Kevin Ellis et al · 2006
Earlier work this paper cites.
“Inductive Biases for Deep Learning of Higher-Level Cognition”
Anirudh Goyal and Yoshua Bengio · 2011
Earlier work this paper cites.
“SemEval-2012 Task 7: Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning”
Andrew. Gordon, Zornitsa Kozareva and Melissa Roemmele · 2012
Earlier work this paper cites.
“Human-level concept learning through probabilistic program induction”
Brenden Lake, Ruslan Salakhutdinov and Joshua Tenenbaum · 2015
Earlier work this paper cites.
“Measuring abstract reasoning in neural networks”
Adam Santoro et al · 2018
Earlier work this paper cites.
“BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”
Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova · 2019
Earlier work this paper cites.
“Decoupled Weight Decay Regularization”
Ilya Loshchilov and Frank Hutter · 2019
Earlier work this paper cites.
“Language models are unsupervised multitask learners”
Alec Radford et al · 2019
Earlier work this paper cites.
“RAVEN: A Dataset for Relational and Analogical Visual REasoNing”
Chi Zhang et al · 2019
Earlier work this paper cites.
“Language Models are Few-Shot Learners”
Tom. Brown et al · 2020
Earlier work this paper cites.
“Transformers as Soft Reasoners over Language”
Peter Clark, Oyvind Tafjord and Kyle Richardson · 2020
Earlier work this paper cites.
“LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning”
Jian Liu et al · 2020
Earlier work this paper cites.
“Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer”
Colin Raffel et al · 2020
Earlier work this paper cites.
“The child as hacker: building more human-like models of learning”, 2020
Joshua Rule · 2020
Earlier work this paper cites.
“Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge”
Alon Talmor et al · 2020
Earlier work this paper cites.
“ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning”
Weihao Yu, Zihang Jiang, Yanfei Dong and Jiashi Feng · 2020
Earlier work this paper cites.
“Communicating Natural Programs to Humans and Machines”
Samuel Acquaviva et al · 2021
Earlier work this paper cites.
“On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?”
Emily. Bender, Timnit Gebru, Angelina McMillan-Major and Shmargaret Shmitchell · 2021
Cited alongside, same era.
“On the Opportunities and Risks of Foundation Models”
Rishi Bommasani et al · 2021
Cited alongside, same era.
“Thinking Fast and Slow in AI”
Grady Booch et al · 2021
Cited alongside, same era.
“Evaluating Large Language Models Trained on Code”
Mark Chen et al · 2021
Cited alongside, same era.
“Toward Causal Representation Learning”
Bernhard Schölkopf et al · 2021
Cited alongside, same era.
“ACRE: Abstract Causal REasoning Beyond Covariation”
Chi Zhang et al · 2021
“Chain-of-Thought Prompting Elicits Reasoning in Large Language Models”
Jason Wei et al · 2022
Later among the works it cites.
“Can Foundation Models Talk Causality?”
Moritz Willig, Matej Zecevic, Devendra Dhami and Kristian Kersting · 2022
Later among the works it cites.
“A systematic evaluation of large language models of code”
Frank. Xu, Uri Alon, Graham Neubig and Vincent Hellendoorn · 2022
Later among the works it cites.
“Language Models as Inductive Reasoners”
Zonglin Yang et al · 2022
Later among the works it cites.
“AbductionRules: Training Transformers to Explain Unexpected Inputs”
Nathan Young, Qiming Bao, Joshua Bensemann and Michael Witbrock · 2022
Later among the works it cites.
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Chiyuan Zhang, Maithra Raghu, Jon. Kleinberg and Samy Bengio · 2021
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“Multi-Step Deductive Reasoning Over Natural Language: An Empirical Study on Out-of-Distribution Generalisation”
Qiming Bao et al · 2022
Cited alongside, same era.
Wenhu Chen, Xueguang Ma, Xinyi Wang and William. Cohen · 2022
Cited alongside, same era.
“PaLM: Scaling Language Modeling with Pathways”
Aakanksha Chowdhery et al · 2022
Cited alongside, same era.
“LoRA: Low-Rank Adaptation of Large Language Models”
Edward. Hu et al · 2022
Cited alongside, same era.
“MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning”
Fangkai Jiao, Yangyang Guo, Xuemeng Song and Liqiang Nie · 2022
Cited alongside, same era.
Jiayao Zhang, Hongming Zhang, Weijie. Su and Dan Roth · 2022
Later among the works it cites.
“Contrastive Learning with Logic-driven Data Augmentation for Logical Reasoning over Text”
Qiming Bao et al · 2023
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“Sparks of Artificial General Intelligence: Early experiments with GPT-4”
Sébastien Bubeck et al · 2023
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“A Survey of Methods, Challenges and Perspectives in Causality”
Gaël Gendron, Michael Witbrock and Gillian Dobbie · 2023
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Albert. Jiang et al · 2023
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“Causal Reasoning and Large Language Models: Opening a New Frontier for Causality”
Emre Kiciman, Robert Ness, Amit Sharma and Chenhao Tan · 2023
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“Self-Refine: Iterative Refinement with Self-Feedback”
Aman Madaan et al · 2023
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OpenAI · 2023
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Linlu Qiu et al · 2023
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“Stanford Alpaca: An Instruction-following LLaMA model”
Rohan Taori et al · 2023
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“Llama 2: Open Foundation and Fine-Tuned Chat Models”
Hugo Touvron et al · 2023
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“LLaMA: Open and Efficient Foundation Language Models”
Hugo Touvron et al · 2023
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“The Alignment Handbook”
Lewis Tunstall et al · 2023
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“Zephyr: Direct Distillation of LM Alignment”
Lewis Tunstall et al · 2023
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“Hypothesis Search: Inductive Reasoning with Language Models”
Ruocheng Wang et al · 2023
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