Large language models (LLMs) are dramatically influencing AI research, spurring discussions on what has changed so far and how to shape the field's future. To clarify such questions, we analyze a new dataset of 16,979 LLM-related arXiv papers, focusing on recent trends in 2023 vs. 2018-2022. First, we study disciplinary shifts: LLM research increasingly considers societal impacts, 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, driving disciplinary expansion. Second, we study industry and academic publishing trends. Surprisingly, industry accounts for a smaller publication share in 2023, largely due to reduced output from Google and other Big Tech companies; universities in Asia are publishing more. Third, we study institutional collaboration: while industry-academic collaborations are common, 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, but there is very little cross-country collaboration. We discuss implications around (1) how to support the influx of new authors, (2) how industry trends may affect academics, and (3) possible effects of (the lack of) collaboration.

翻译：大型语言模型（LLMs）正深刻影响着人工智能研究，引发关于现有变化及未来方向的探讨。为厘清这些问题，我们分析了包含16,979篇LLM相关arXiv论文的新数据集，重点关注2023年与2018-2022年间的近期趋势。首先，我们研究学科变迁：LLM研究日益关注社会影响，体现在向arXiv"计算机与社会"子分类提交的LLM论文数量增长了20倍。大量新作者——2023年半数第一作者——正从计算机科学非自然语言处理领域涌入，推动学科扩张。其次，我们分析产业界与学术界发表趋势。令人意外的是，2023年产业界发表份额有所下降，主要源于谷歌及其他大型科技公司产出减少；与此同时，亚洲高校发表量持续增长。第三，我们研究机构合作模式：尽管产学研合作普遍存在，但此类合作往往聚焦于产业主导的相同课题，而非弥合领域差异。高产机构均位于美国或中国，但跨国合作极为稀缺。我们围绕以下三方面展开讨论：(1)如何支持新作者涌入，(2)产业趋势可能对学术界的影响，以及(3)（缺乏）合作可能产生的效应。