NLLG Quarterly arXiv Report 06/23: What are the most influential current
  AI Papers?
Steffen Eger Christoph Leiter Jonas Belouadi Ran Zhang Aida Kostikova Daniil Larionov Yanran Chen Vivian Fresen
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.