# Analyzing 16,193 LLM Papers for Fun and Profits

arXiv: https://arxiv.org/abs/2504.08619 · HTML: https://mlsanity.com/paper/2504.08619/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 11 Apr 2025.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://mlsanity.com/paper/2212.09741/) (2022)
- [OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts](https://mlsanity.com/paper/2205.01833/) (2022)
- [A Bibliometric Review of Large Language Models Research from 2017 to 2023](https://mlsanity.com/paper/2304.02020/) (2023)
- [Examining Citations of Natural Language Processing Literature](https://mlsanity.com/paper/2005.00912/) (2020)
- [A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why?](https://mlsanity.com/paper/2305.12920/) (2023)
- [A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations](https://mlsanity.com/paper/1704.05150/) (2017)

## Built on by (most cited first, 4 of 4)

- [Real Money, Fake Models: Deceptive Model Claims in Shadow APIs](https://mlsanity.com/paper/2603.01919/) (2026)
- [Specification and Detection of LLM Code Smells](https://mlsanity.com/paper/2512.18020/) (2025)
- [Fairness in Multi-Agent Systems for Software Engineering: An SDLC-Oriented Rapid Review](https://mlsanity.com/paper/2604.13103/) (2026)
- [LLM Code Smells: A Taxonomy and Detection Approach](https://mlsanity.com/paper/2605.22976/) (2026)

## Abstract (arXiv)

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.