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This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys in rapidly evolving fields like artificial intelligence.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani · 2018
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Self-attention with structural position representations
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Retrieval-augmented generation for knowledge-intensive nlp tasks
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Progressive generation of long text with pretrained language models
Bowen Tan, Zichao Yang, Maruan Al-Shedivat, Eric P. Xing, and Zhiting Hu · 2021
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Recursively summarizing books with human feedback
Jeff Wu, Long Ouyang, Daniel M Ziegler, Nisan Stiennon, Ryan Lowe, Jan Leike, and Paul Christiano · 2021
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Transformers in vision: A survey
Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah · 2022
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Large pre-trained language models contain human-like biases of what is right and wrong to do
Patrick Schramowski, Cigdem Turan, Nico Andersen, Constantin A Rothkopf, and Kristian Kersting · 2022
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Teaching language models to support answers with verified quotes
Jacob Menick, Maja Trebacz, Vladimir Mikulik, John Aslanides, Francis Song, Martin Chadwick, Mia Glaese, Susannah Young, Lucy Campbell-Gillingham, Geoffrey Irving, et al · 2022
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A survey on evaluation of large language models
Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al · 2023
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A survey of large language models
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Segment anything
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Llama: Open and efficient foundation language models
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Extending context window of large language models via positional interpolation
Loogle: Can long-context language models understand long contexts?
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Recurrentgpt: Interactive generation of (arbitrarily) long text
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Context-faithful prompting for large language models
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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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Challenges and applications of large language models
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Large language models can be easily distracted by irrelevant context
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How long can context length of open-source llms truly promise?
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Survey of hallucination in natural language generation
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Long-context llms struggle with long in-context learning
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Distilling text style transfer with self-explanation from llms
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