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Large Language Models (LLMs) are a powerful technology that augment human skill to create new opportunities, akin to the development of steam engines and the internet.
“Energy and Policy Considerations for Deep Learning in NLP”, 2019
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“The Cost of Training NLP Models: A Concise Overview”
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“Language Models are Few-Shot Learners”, 2020
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“Measuring Massive Multitask Language Understanding”, 2021
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“BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”, 2019
Jacob Devlin et al · 2019
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Roy Schwartz et al · 2020
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“On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?”
Emily. Bender et al · 2021
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“Training compute-optimal large language models”
Jordan Hoffmann et al · 2022
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“OPT: Open Pre-trained Transformer Language Models”, 2022
Susan Zhang et al · 2022
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“On the Opportunities and Risks of Foundation Models”, 2022
Rishi Bommasani et al · 2022
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“The Turing Trap: The Promise and Peril of Human-Like Artificial Intelligence”, 2022
Erik Brynjolfsson · 2022
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“PaLM: Scaling Language Modeling with Pathways”, 2022
Aakanksha Chowdhery et al · 2022
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“Scaling Instruction-Finetuned Language Models”, 2022
Hyung Chung et al · 2022
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OECD, 2022
“Venture capital investments (market statistics)” · 2022
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“Quantifying and alleviating political bias in language models”
Ruibo Liu et al · 2022
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Zijian Ding et al · 2023
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“Large language models in medicine”
Arun Thirunavukarasu et al · 2023
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“Generating Novel Leads for Drug Discovery using LLMs with Logical Feedback”
Shreyas Brahmavar et al · 2023
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“14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon”
Kevin Jablonka et al · 2023
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“Chatlaw: Open-source legal large language model with integrated external knowledge bases”
Jiaxi Cui et al · 2023
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“Empowering education with llms-the next-gen interface and content generation”
Steven Moore et al · 2023
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“Evaluating reading comprehension exercises generated by LLMs: A showcase of ChatGPT in education applications”
Changrong Xiao et al · 2023
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“‘It’s destroyed me completely’: Kenyan moderators decry toll of training of AI models”
Niamh Rowe · 2023
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“GPT-4 Technical Report”, 2023
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“BloombergGPT: A Large Language Model for Finance”, 2023
Shijie Wu et al · 2023
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“Multilingual machine translation with large language models: Empirical results and analysis”
Wenhao Zhu et al · 2023
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“Document-level machine translation with large language models”
Longyue Wang et al · 2023
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Xinyi Hou et al · 2023
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“Codeplan: Repository-level coding using llms and planning”
Ramakrishna Bairi et al · 2023
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“Large language models for robotics: A survey”
Fanlong Zeng et al · 2023
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“The economic potential of Generative AI: The Next Productivity Frontier”
Michael Chui et al · 2023
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“Anglekindling: Supporting journalistic angle ideation with large language models”
Savvas Petridis et al · 2023
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“Artificial intelligence practices in everyday news production: The case of South Africa’s mainstream newsrooms”
Allen Munoriyarwa, Sarah Chiumbu and Gilbert Motsaathebe · 2023
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“MEDITRON-70B: Scaling Medical Pretraining for Large Language Models”, 2023
Zeming Chen et al · 2023
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“MathPrompter: Mathematical Reasoning using Large Language Models”, 2023
Shima Imani, Liang Du and Harsh Shrivastava · 2023
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dealroom.co, 2023
“Locations funding heatmap)” · 2023
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“Bias and Fairness in Large Language Models: A Survey”, 2023
Isabel. Gallegos et al · 2023
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Shangbin Feng et al · 2023
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“Towards Measuring the Representation of Subjective Global Opinions in Language Models”, 2023
Esin Durmus et al · 2023
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“ChatGPT and large language models in academia: opportunities and challenges”
Jesse Meyer et al · 2023
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“DebugBench: Evaluating Debugging Capability of Large Language Models”
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“LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models”, 2024
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