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The vast number of parameters in large language models (LLMs) endows them with remarkable capabilities, allowing them to excel in a variety of natural language processing tasks.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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Finite-time analysis of the multiarmed bandit problem
Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer. 2002 · 2002
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Hippocampus: cognitive processes and neural representations that underlie declarative memory
Howard Eichenbaum. 2004 · 2004
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Fact or fiction: Verifying scientific claims
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Understanding bag-of-words model: a statistical framework
Yin Zhang, Rong Jin, and Zhi-Hua Zhou. 2010 · 2010
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Semantic parsing on freebase from question-answer pairs
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Efficient estimation of word representations in vector space
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Taxonomy construction using syntactic contextual evidence
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Towards time-aware knowledge graph completion
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Learning term embeddings for taxonomic relation identification using dynamic weighting neural network
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Attention is all you need
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The web as a knowledge-base for answering complex questions
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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
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Large language models and the perils of their hallucinations
Razvan Azamfirei, Sapna R Kudchadkar, and James Fackler. 2023 · 2023
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Significant-gravitas/auto-gpt: An experimental open-source attempt to make gpt-4 fully autonomous
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How should pre-trained language models be fine-tuned towards adversarial robustness?
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LangChain
Harrison Chase. 2022 · 2022
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What language model to train if you have one million gpu hours?
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Llama: Open and efficient foundation language models
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