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Humans generally acquire new skills without compromising the old; however, the opposite holds for Large Language Models (LLMs), e.g., from LLaMA to CodeLLaMA.
Language models are few-shot learners
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Bert rediscovers the classical nlp pipeline
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Hellaswag: Can a machine really finish your sentence?
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Fine-tuning language models from human preferences
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Don’t stop pretraining: Adapt language models to domains and tasks
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Measuring massive multitask language understanding
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On the transformer growth for progressive bert training
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Alfworld: Aligning text and embodied environments for interactive learning
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Think you have solved question answering? try arc, the ai2 reasoning challenge
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Efficient training of bert by progressively stacking
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Parameter-efficient transfer learning for nlp
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Claudette: an automated detector of potentially unfair clauses in online terms of service
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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How does bert answer questions? a layer-wise analysis of transformer representations
Betty Van Aken, Benjamin Winter, Alexander Löser, and Felix A Gers. 2019 · 2019
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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 · 2020
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Program synthesis with large language models
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Lexglue: A benchmark dataset for legal language understanding in english
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Training verifiers to solve math word problems
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How abilities in large language models are affected by supervised fine-tuning data composition
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Making llama see and draw with seed tokenizer
Yuying Ge, Sijie Zhao, Ziyun Zeng, Yixiao Ge, Chen Li, Xintao Wang, and Ying Shan. 2023 · 2023
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Continual pre-training of large language models: How to (re) warm your model?
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023 · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al. 2021 · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021 · 2021
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. 2022 · 2022
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Training compute-optimal large language models
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Training language models to follow instructions with human feedback
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Slimorca: An open dataset of gpt-4 augmented flan reasoning traces, with verification
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Code llama: Open foundation models for code
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2x faster language model pre-training via masked structural growth
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Metamath: Bootstrap your own mathematical questions for large language models
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Adding conditional control to text-to-image diffusion models
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Judging llm-as-a-judge with mt-bench and chatbot arena
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Lima: Less is more for alignment
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Gemma: Open models based on gemini research and technology
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