Analyzing 16,193 LLM Papers for Fun and Profits

2025-04-11

AI Summary

This study systematically investigates how large language models (LLMs) have reshaped the computer science academic ecosystem from 2019 to 2024. Addressing four core questions—shifts in top-tier conference research themes, evolution of emerging subfields, disparities in contributions between academia and industry, and national-level developmental trajectories—the work introduces the first multidimensional analytical framework spanning 77 premier conferences and 16,193 papers. It integrates bibliometrics, LDA topic modeling, metadata mining, and time-series analysis. The study quantifies dynamic LLM-topic penetration rates across conferences, uncovers a structural bifurcation between foundational theory and applied deployment, and distills ten original, empirically grounded insights into the AI research ecosystem. These findings provide rigorous evidence to inform AI research policy and disciplinary evolution.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Applications

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Analyzing LLM-driven topic shifts in computer science conferences
• Identifying LLM-related topic growth across diverse research fields
• Exploring institutional and national influences on LLM development trends

Innovation

• Analyzing LLM papers across 77 conferences
• Using topic modeling to identify growth areas
• Comparing academic and industrial contributions