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（LLM）正深刻影响人工智能研究格局，引发关于其已带来的变革及如何引导该领域未来发展的讨论。为厘清这些问题，我们分析了一个包含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，我们考察学科演变：LLM研究日益关注社会影响，体现在向"计算机与社会"子领域提交的LLM论文数量增长了20倍。大量新作者——2023年半数论文的第一作者——正从计算机科学中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出版趋势。出人意料的是，2023年产业界论文发表份额有所下降，这主要源于谷歌及其他大型科技公司产出减少；而亚洲高校则贡献了更多论文。第三，我们探讨机构合作模式：尽管产学合作较为普遍，但这些合作往往聚焦于产业界擅长的主题，而非促进双方差异互补。发表论文最多的机构均位于美国或中国，但跨国合作极为有限。最后，我们讨论以下启示：（1）如何支持新作者大量涌入；（2）产业界趋势对学术界的影响；（3）合作（缺失）可能带来的效应。