Related papers: Examining Citations of Natural Language Processing…

Natural Language Processing (NLP) is undergoing constant transformation, as Large Language Models (LLMs) are driving daily breakthroughs in research and practice. In this regard, tracking the progress of NLP research and automatically…

We present empirical data on frequency and pattern of misprints in citations to twelve high-profile papers. We find that the distribution of misprints, ranked by frequency of their repetition, follows Zipf's law. We propose a stochastic…

Traditionally, the number of citations that a scholarly paper receives from other papers is used as the proxy of its scientific impact. Yet citations can come from domains outside the scientific community, and one such example is through…

The scale and scope of scholarly articles today are overwhelming human researchers who seek to timely digest and synthesize knowledge. In this paper, we seek to develop natural language processing (NLP) models to accelerate the speed of…

This study explores the connection between patent citations and scientific publications across six fields: Biochemistry, Genetics, Pharmacology, Engineering, Mathematics, and Physics. Analysing 117,590 papers from 2014 to 2023, the research…

Researchers cite works for a variety of reasons, including some having nothing to do with acknowledging influence. The distribution of different citation types in the literature, and which papers attract which types, is poorly understood.…

Against the background of what has been termed a reproducibility crisis in science, the NLP field is becoming increasingly interested in, and conscientious about, the reproducibility of its results. The past few years have seen an…

Large language models (LLMs) like ChatGPT can generate and revise text with human-level performance. These models come with clear limitations: they can produce inaccurate information, reinforce existing biases, and be easily misused. Yet,…

Natural Language Processing (NLP) is revolutionising the way both professionals and laypersons operate in the legal field. The considerable potential for NLP in the legal sector, especially in developing computational assistance tools for…

In order to answer questions about top conference publication patterns, citation data is collected and analyzed for several computer science conferences, with focus on computer vision and graphics. Both top and second tier conferences are…

Natural Language Processing (NLP) aims to analyze text or speech via techniques in the computer science field. It serves applications in the domains of healthcare, commerce, education, and so on. Particularly, NLP has been widely applied to…

It is well known in bibliometrics that the average number of citations per paper differs greatly between the various disciplines. The differing citation culture (in particular the different average number of references per paper and thereby…

This study investigates how Large Language Models (LLMs) are influencing the language of academic papers by tracking 12 LLM-associated terms across six major scholarly databases (Scopus, Web of Science, PubMed, PubMed Central (PMC),…

This paper aims to analyze the extent to which inventive activity relies on open science. In other words, it investigates whether inventors utilize Open Access (OA) publications more than subscription-based ones, especially given that some…

This paper defines and explores the direct citations between citing publications (DCCPs) of a publication. We construct an ego-centered citation network for each paper that contains all of its citing papers and itself, as well as the…

The impact and originality are two critical dimensions for evaluating scientific publications, measured by citation and disruption metrics respectively. Despite the extensive effort made to understand the statistical properties and…

Lack of repeatability and generalisability are two significant threats to continuing scientific development in Natural Language Processing. Language models and learning methods are so complex that scientific conference papers no longer…

Large language models (LLMs) bring unprecedented flexibility in defining and executing complex, creative natural language generation (NLG) tasks. Yet, this flexibility brings new challenges, as it introduces new degrees of freedom in…

Large language models (LLMs) have been increasingly applied to a wide range of tasks, from natural language understanding to code generation. While they have also been used to assist in bibliographic recommendation, the hallucination of…

Large language models (LLMs) are known to generate plausible but false information across a wide range of contexts, yet the real-world magnitude and consequences of this hallucination problem remain poorly understood. Here we leverage a…