Rajiv Movva, a PhD student in Computer Science at Cornell Tech University, discusses the findings of his research on arXiv publication patterns for LLMs. He shares insights on the increase in LLMs research and proportions of papers published by universities, organizations, and industry leaders. He highlights the focus on the social impact of LLMs and explores exciting applications in education.
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Quick takeaways
The analysis of LLM publication patterns reveals a surge in research, with a focus on the social impact of LLMs in fields such as healthcare, law, and education.
While industry leaders like Google dominate in terms of published papers, a significant portion of LLM research comes from academic institutions, highlighting the importance of collaboration between academia and industry for advancing research and mitigating potential negative implications of competition.
Deep dives
Publication trends and research focus
The podcast episode discusses the growing trend of research publications on large language models (LLMs) and their impact on various fields. The host mentions using the archive.org - ARXIV.org website as a data source for finding guest speakers. The show focuses on analyzing the increasing number of publications related to LLMs and their effects on research. The episode highlights the shift in focus from narrow NLP tasks to broader applications, such as healthcare, law, and education. The data set consists of 17,000 papers from the archive, and the analysis reveals a surge in LLM-related research in recent years.
Academic and industry contributions
The podcast discusses the differences between academic and industry contributions to LLM research. It notes that industry institutions primarily focus on methods work, like improving efficiency and performance of models, while academics focus more on applications in diverse fields like healthcare, law, and education. The analysis reveals that while larger industry players like Microsoft and Google dominate the number of papers published, a significant portion of LLM research comes from academic institutions. The episode emphasizes the significance of collaboration between industries and universities, which is relatively limited but important for advancing research and avoiding potential negative implications of competition.
Societal impacts and implications
The episode explores the societal impacts and implications of LLMs. It highlights the rise of research on the societal implications of technology, driven by concerns about algorithmic bias, ethical considerations, and long-term risks. The analysis reveals a growing focus on the societal applications of LLMs, including healthcare, law, education, and recommender systems. The podcast also touches on the lack of collaboration between the AI ethics and AI safety communities, which have different research priorities. The episode suggests that more empirical research is needed to fully understand the effects of LLMs on areas like education, and calls for interdisciplinary collaboration in studying the societal impacts of these technologies.
Research landscape and future directions
The podcast delves into the challenges and methodologies used in studying LLM research. It mentions the use of clustering and labeling techniques to analyze the vast number of papers, with abstracts providing a basis for topic representation. The episode describes the differences in research focuses between academia and industry, as well as the usage of large language models in various sectors. The episode calls for further exploration of topics like collaboration between different institutions, the balance between ethics and safety concerns in LLM research, and the potential for LLMs to democratize access to healthcare and improve health equity. The host expresses a general interest in researching the evolving landscape of LLMs and related technologies.
Today, we are joined by Rajiv Movva, a PhD student in Computer Science at Cornell Tech University. His research interest lies in the intersection of responsible AI and computational social science. He joins to discuss the findings of this work that analyzed LLM publication patterns.
He shared the dataset he used for the survey. He also discussed the conditions for determining the papers to analyze. Rajiv shared some of the trends he observed from his analysis. For one, he observed there has been an increase in LLMs research. He also shared the proportions of papers published by universities, organizations, and industry leaders in LLMs such as OpenAI and Google. He mentioned the majority of the papers are centered on the social impact of LLMs. He also discussed other exciting application of LLMs such as in education.
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