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Will Aitken

dblp:332/0142 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Language models and text generation · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Language models and text generation
pre-trained language model
0.812024
Collaboration or Corporate Capture? Quantifying NLP's Reliance on Industry Artifacts and Contributions · ACL (1) 2024
Computational social science and digital humanities
science of science
0.212024
Collaboration or Corporate Capture? Quantifying NLP's Reliance on Industry Artifacts and Contributions · ACL (1) 2024

Methods — techniques the papers use, named apart from their topics

survey · 1.5citation analysis · 1.5
YearPublicationVenuePosition
2024 Collaboration or Corporate Capture? Quantifying NLP's Reliance on Industry Artifacts and Contributions
abstract
Impressive performance of pre-trained models has garnered public attention and made news headlines in recent years.Almost always, these models are produced by or in collaboration with industry.Using them is critical for competing on natural language processing (NLP) benchmarks and correspondingly to stay relevant in NLP research.We surveyed 100 papers published at EMNLP 2022 to determine the degree to which researchers rely on industry models, other artifacts, and contributions to publish in prestigious NLP venues and found that the ratio of their citation is at least three times greater than what would be expected.Our work serves as a scaffold to enable future researchers to more accurately address whether: 1) Collaboration with industry is still collaboration in the absence of an alternative or 2) if NLP inquiry has been captured by the motivations and research direction of private corporations.
Will Aitken, Mohamed Abdalla 0001, Karen Rudie, Catherine Stinson
ACL (1)1