Adapting Large Language Models Via | PDF | Reading Comprehension | Learning
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NetLLM: Adapting Large Language Models For Networking | AI Research Paper Details
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 are dramatically influencing AI research%2C spurring discussions on what has changed so far and how to shape the field's future. To clarify such questions%2C we analyze a new dataset of 16%2C979 LLM-related arXiv papers%2C focusing on recent trends in 2023 vs. 2018-2022. First%2C we study disciplinary shifts: LLM research increasingly considers societal impacts%2C evidenced by 20x growth in LLM submissions to the Computers and Society sub-arXiv. An influx of new authors -- half of all first authors in 2023 -- are entering from non-NLP fields of CS%2C driving disciplinary expansion. Second%2C we study industry and academic publishing trends. Surprisingly%2C industry accounts for a smaller publication share in 2023%2C largely due to reduced output from Google and other Big Tech companies%3B universities in Asia are publishing more. Third%2C we study institutional collaboration: while industry-academic collaborations are common%2C they tend to focus on the same topics that industry focuses on rather than bridging differences. The most prolific institutions are all US- or China-based%2C but there is very little cross-country collaboration. We discuss implications around (1) how to support the influx of new authors%2C (2) how industry trends may affect academics%2C and (3) possible effects of (the lack of) collaboration.?resize=91,91 "Topics, Authors, And Institutions In Large Language Model Research: Trends From 17K ArXiv Papers ...")
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