Analyzing 16,193 LLM Papers for Fun and Profits
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866910916745887744 |
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| author | Xia, Zhiqiu Zhu, Lang Li, Bingzhe Chen, Feng Li, Qiannan Liao, Chunhua Wang, Feiyi Liu, Hang |
| author_facet | Xia, Zhiqiu Zhu, Lang Li, Bingzhe Chen, Feng Li, Qiannan Liao, Chunhua Wang, Feiyi Liu, Hang |
| contents | Large Language Models (LLMs) are reshaping the landscape of computer science research, driving significant shifts in research priorities across diverse conferences and fields. This study provides a comprehensive analysis of the publication trend of LLM-related papers in 77 top-tier computer science conferences over the past six years (2019-2024). We approach this analysis from four distinct perspectives: (1) We investigate how LLM research is driving topic shifts within major conferences. (2) We adopt a topic modeling approach to identify various areas of LLM-related topic growth and reveal the topics of concern at different conferences. (3) We explore distinct contribution patterns of academic and industrial institutions. (4) We study the influence of national origins on LLM development trajectories. Synthesizing the findings from these diverse analytical angles, we derive ten key insights that illuminate the dynamics and evolution of the LLM research ecosystem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08619 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Analyzing 16,193 LLM Papers for Fun and Profits Xia, Zhiqiu Zhu, Lang Li, Bingzhe Chen, Feng Li, Qiannan Liao, Chunhua Wang, Feiyi Liu, Hang Digital Libraries Computation and Language Large Language Models (LLMs) are reshaping the landscape of computer science research, driving significant shifts in research priorities across diverse conferences and fields. This study provides a comprehensive analysis of the publication trend of LLM-related papers in 77 top-tier computer science conferences over the past six years (2019-2024). We approach this analysis from four distinct perspectives: (1) We investigate how LLM research is driving topic shifts within major conferences. (2) We adopt a topic modeling approach to identify various areas of LLM-related topic growth and reveal the topics of concern at different conferences. (3) We explore distinct contribution patterns of academic and industrial institutions. (4) We study the influence of national origins on LLM development trajectories. Synthesizing the findings from these diverse analytical angles, we derive ten key insights that illuminate the dynamics and evolution of the LLM research ecosystem. |
| title | Analyzing 16,193 LLM Papers for Fun and Profits |
| topic | Digital Libraries Computation and Language |
| url | https://arxiv.org/abs/2504.08619 |