NLLG Quarterly arXiv Report 06/23: What are the most influential current AI Papers?

TL;DR

This report identifies the most influential AI papers from arXiv in the first half of 2023, highlighting trends in NLP and ML, especially the prominence of Large Language Models and ChatGPT, to help researchers navigate current developments.

Contribution

It provides a curated list of the top 40 most popular papers based on normalized citations, analyzing their topics, characteristics, and trends in AI research for early 2023.

Findings

NLP papers constitute around 60% of top papers.

Large Language Models and ChatGPT dominate early 2023 research.

Top papers focus on LLM efficiency, evaluation, ethics, and problem-solving.

Abstract

The rapid growth of information in the field of Generative Artificial Intelligence (AI), particularly in the subfields of Natural Language Processing (NLP) and Machine Learning (ML), presents a significant challenge for researchers and practitioners to keep pace with the latest developments. To address the problem of information overload, this report by the Natural Language Learning Group at Bielefeld University focuses on identifying the most popular papers on arXiv, with a specific emphasis on NLP and ML. The objective is to offer a quick guide to the most relevant and widely discussed research, aiding both newcomers and established researchers in staying abreast of current trends. In particular, we compile a list of the 40 most popular papers based on normalized citation counts from the first half of 2023. We observe the dominance of papers related to Large Language Models (LLMs) and specifically ChatGPT during the first half of 2023, with the latter showing signs of declining popularity more recently, however. Further, NLP related papers are the most influential (around 60\% of top papers) even though there are twice as many ML related papers in our data. Core issues investigated in the most heavily cited papers are: LLM efficiency, evaluation techniques, ethical considerations, embodied agents, and problem-solving with LLMs. Additionally, we examine the characteristics of top papers in comparison to others outside the top-40 list (noticing the top paper's focus on LLM related issues and higher number of co-authors) and analyze the citation distributions in our dataset, among others.