# Predicting Research Trends From Arxiv

arXiv: https://arxiv.org/abs/1903.02831 · HTML: https://mlsanity.com/paper/1903.02831/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [ChatGPT: A Meta-Analysis after 2.5 Months](https://mlsanity.com/paper/2302.13795/) (2023)
- [NLLG Quarterly arXiv Report 06/23: What are the most influential current AI Papers?](https://mlsanity.com/paper/2308.04889/) (2023)
- [NLLG Quarterly arXiv Report 09/24: What are the most influential current AI Papers?](https://mlsanity.com/paper/2412.12121/) (2024)
- [Is there really a Citation Age Bias in NLP?](https://mlsanity.com/paper/2401.03545/) (2024)
- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)

## Abstract (arXiv)

We perform trend detection on two datasets of Arxiv papers, derived from its machine learning (cs.LG) and natural language processing (cs.CL) categories. Our approach is bottom-up: we first rank papers by their normalized citation counts, then group top-ranked papers into different categories based on the tasks that they pursue and the methods they use. We then analyze these resulting topics. We find that the dominating paradigm in cs.CL revolves around natural language generation problems and those in cs.LG revolve around reinforcement learning and adversarial principles. By extrapolation, we predict that these topics will remain lead problems/approaches in their fields in the short- and mid-term.