The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research (2023)

Abstract: Recent advances in deep learning methods for natural language processing (NLP) have created new business opportunities and made NLP research critical for industry development. As one of the big players in the field of NLP, together with governments and universities, it is important to track the influence of industry on research. In this study, we seek to quantify and characterize industry presence in the NLP community over time. Using a corpus with comprehensive metadata of 78,187 NLP publications and 701 resumes of NLP publication authors, we explore the industry presence in the field since the early 90s. We find that industry presence among NLP authors has been steady before a steep increase over the past five years (180% growth from 2017 to 2022). A few companies account for most of the publications and provide funding to academic researchers through grants and internships. Our study shows that the presence and impact of the industry on natural language processing research are significant and fast-growing. This work calls for increased transparency of industry influence in the field.

## introduction figure 1 : proportion of papers in the acl anthology with at least one industry affiliation @xcite . it is in the interest of any community to pause and reflect on who has the power, what their interests and values might be, and how they are influencing research. thus, this recent period of great change in nlp research merits a reflection on the changing role of industry in research, as well as their implications on society at large. such a reflection will naturally require many broad approaches studying the socio-, economic-, and political forces at play in combination with technology. our work aims at facilitating such a reflection by quantifying and characterizing the presence of big tech in nlp research, especially during this time of change. as such, it puts a spotlight on the changing role of industry in nlp research. the changing role of industry in nlp research. our analysis of papers published in the acl anthology with at least one company affiliation (figure 1 -see section 4.1) confirms that we are currently experiencing dramatic change. as highlighted by the industry track at naacl foot_0 , industry research can offer a focus on applied research and technical challenges such as scaling robustness. by establishing large industrial research labs or through collaborations with academics, the industry has fostered increased attention to these research topics. favorable assessments. increased focus on nlp from industry can result in seemingly positive outcomes such as increased funding (e.g., through grants, awards, and scholarships). more generally, in the field of policy research, industry funding is largely viewed as positive @xcite , with industry-funded research having been shown to result in more patents @xcite , and more publications overall @xcite . warnings. however, in the philosophy of science, industry funding is largely viewed as "corrosive to scientific inquiry" @xcite . this view has been increasingly reflected in the work of ai ethics researchers who have highlighted concerns regarding the impact of such a large and growing industry presence @xcite @xcite . @xcite raise concerns about the lack of impartiality of research and undue influence on research questions. others raise concerns about the centralization of resources, where only those with industry affiliations can research the latest language technologies @xcite . there is also concern about the co-option of ai ethics research to prevent, water-down, or negatively affect legislation and our understanding of the societal impacts of ai applications @xcite @xcite . this study. the focus of this study is not to debate the benefits or harms of increased industry presence in the nlp community. instead, we seek to quantify and characterize industry presence in nlp. using both manual annotations of the curriculum vitae (cvs) of authors who have published at a major nlp conference (namely, the 60th annual meeting of the association for computational linguistics -acl 2022) and automated analysis of all papers within the acl anthology to date, we explore the industry presence in nlp using five research questions that serve as a guide for our exploration: ## related work scientometrics is a field of study which explores scientific research from a quantitative perspective @xcite . tracing back to the mid-20th century (price, 1961), efforts in this field have focused on measuring the impact of research @xcite , mapping science to understand relationships between fields @xcite , the volume of work being published @xcite , how scientific concepts have changed over time @xcite , and how scientists have changed over time @xcite . focused on nlp, there has been a healthy amount of research conducted on studying the field; many researchers have shared open-sourced datasets that can be used to study the growth and change in nlp @xcite @xcite . @xcite developed the nlp4nlp corpus, a collection of nlp articles published from 1965 to 2015 in 34 nlp conferences and journals. with this dataset, they provided an extensive analysis of references, citations, and authorship. similarly, mohammad (2020c) created nlp scholar, a dataset that combined information from the acl anthology with google scholar, which was used to study the volume of research, the citation practices patterns in the field (mohammad, 2020a), and demographic changes in the field @xcite recently, a few papers have attempted to quantify the presence of industry within computer science. @xcite quantified both the number of professors at four schools who had past financial relationships with industry and the funding patterns of the biggest machine learning conferences over the past five years. ahmed and wahed (2020) created a dataset of 171,000 research papers from many computer science conferences to study industry participation rates over time. they found that while industry participation has been increasing over time, these collaborations have primarily remained between large companies and elite research institutions (i.e., those ranked 1-50 on qs world university rankings 2 ). our work stands out from previous work in multiple ways. unlike most scientometrics works in the field of nlp, we are the first to focus on industry presence. we narrow the focus of ahmed and wahed (2020) to solely nlp, but extend the scope to all conferences within the acl anthology. furthermore, our automatic analysis asks questions not explored by previous work (e.g., exploring industryacademia relationships and impact as measured by citations). ## methodology to accurately measure industry presence in nlp research, the quality and amount of information about affiliations, funding, and employment are limited. in particular, few authors report funding or employment in a paper's acknowledgment section. we retrieve high-quality metadata about authors by examining the cvs and webpages of 681 authors who published in the acl 2022 conference. this part of the analysis will be called manual analysis, and complements the automatic analysis. to extend our study to more venues and a larger time frame, we automatically extracted information from papers for over 34 venues in the acl anthology from 1965 to 2022. this part will be referred to as the automatic analysis. although the automatic analysis cannot gather the same features as the manual analysis, it provides a historical perspective on the industry presence that can be 2 www.topuniversities.com/qs-world-university-rankings back-tested using the manual part. below, we detail how we collected, processed, and annotated the data for automatic and manual analyses. ## industry presence in nlp research the manually curated data and the automatically extracted metadata allow for exploring a rich set of questions regarding the presence of big tech in nlp research. the following subsections explore a different aspect of the industry presence in nlp and rely on both manual and automated methods. ## final considerations the role of industry in shaping the direction of nlp research. in this work, we quantified the presence of industry within the field of nlp. in explicitly highlighting how our field has interacted with industry in the past, we enable ourselves to make decisions for the future. throughout this study, we observed that many researchers work in countries with vital technology industries and infrastructure, such as the us, china, japan, and germany. additionally, these companies often collaborate with universities in these regions, providing funding and other resources for nlp research. advances made by industry have a significant effect on nlp, as well as on broader technology and society (e.g., widespread use of translation apps and virtual assistants). overall, the presence of big tech companies in nlp research has contributed to the growth and advancement of the field. these companies provide significant funding and resources for nlp research, not least by sponsoring conferences, and their collaborations with universities and researchers have led to many important innovations and breakthroughs. it should be noted though, that big tech also benefits from their collaborations with universities and governments through tax benefits, grants, labor, recruiting, and a wide-array of open source research. there exist fears that such tremendous infrastructure can lead to monopolies (e.g., microsoft has an exclusive license on gpt-3, and the majority of research labs lack the infrastructure to reproduce these models.). further, research that requires massive infrastructure is difficult to reproduce for most research labs. when data, trained models, or results are not publicly shared, their transparency gets compromised. one example is the openai gpt-3 model that microsoft exclusively licensed for @xmath047b in revenue per year (44% of facebook's revenue). gpt-3 was not accessible for almost a year and was then released to the general public through an api endpoint that gives access to some, but not all, functionalities of the model. nlp and ai research are seen as innovation drivers for core business models -and are valued extremely highly as such. in contrast, the value of the research for fundamental science may be sidelined; some symptoms of this include the widespread adoption of technologies before peer review of the underlying science -either because the paper was simply put up on arxiv (bert @xcite -for a few years) or because the technology was released only in a website (chatgpt 9 ). technology companies have also had a relatively poor record of developing an inclusive work environment @xcite . thus, there are concerns about their influence impacting the diversity of researchers in nlp. several studies have shown that a diversity of participants is crucial for developing technologies that are beneficial for all, and not just those with power 10 (rao and tilt, 2016; reiche, 2017; nielsen et al., 2017). recommendations. we advocate for increased transparency in industry dealings within nlp research to help facilitate future decision-making and keep track of industry presence in the field. the nlp community organizers should push for standardized databases of author-industry interactions, similar to the orcid initiative @xcite . we think that a centralized database of all financial relationships would go a long way to enabling the study of the effects of industry presence in our field. the field should also take steps to prevent the monopolization of certain kinds of research that requires huge infrastructure (e.g., the creation of publicly controlled research infrastructure possibly funded by taxes on industry). ## limitations 6.1 manual analysis there are a few limitations with the manual analysis. first, as manually annotating hundreds of cvs is a time-intensive process, our analysis only represents a single point of time for a single conference. while this limitation is offset by the automated analysis for certain findings (e.g., affiliation analysis), stronger conclusions regarding other findings (e.g., faculty funding through grants) cannot necessarily be drawn (though our findings are supported by previous works @xcite ). the findings of our manual analysis that relied solely on cv analysis may also be systematically biased by the decision of faculty and graduate students to make information about themselves publicly available. for many features, there were many authors with "unknown", and it may be possible that this is not random but systematic (e.g., those with more industry ties choose not to publish cvs or disclose their grant history or vice-versa). at the same time, those who opted out of our manual annotation could have done so for reasons related to information that could be found on their cvs, and this could theoretically bias the results. however, we do not think that those opting out of our study had a significant impact as they represented less than 3% of the study population. while our annotators followed the same annotation methodology (e.g., how to find authors and annotate each author), as observed in our agreement analysis, our search engines often returned a different ordering of websites for the same query for different users. as annotators only had to examine the main webpage of the author, it is possible that reordering the results affected which web pages were viewed. however, most annotations disagreements were between a given value and "unknown" rather than incorrect attribution of a feature. ## ethical considerations our study does not qualify for irb review as our study data are non-sensitive information contained in publicly available information sources. however, as part of the manual analysis, we had to create a file containing identifying information (names and website urls to online cvs). the gdpr requires that the randomly selected participants be informed of the creation of such a file and be provided with the means to withdraw from the study and/or modify information about themselves. in compliance with the gdpr, all participants were sent an email offering them the possibility to withdraw from the study or provide an updated cv if interested. nine participants requested the removal of their data, which we did immediately. additionally, 11 participants could not be reached and were withdrawn from the study. six participants sent us their updated cvs, so that we could take them into account and be more precise. participants were initially reached using the emails listed in the papers, which we extracted automatically using grobid (romary and lopez, 2015). 94 emails could not be delivered. we doubled checked the correctness of emails by looking at the first page of the published papers (and author websites if no email was present in the paper) to correct errors or find alternative emails. after correcting any mistakes, gathering alternative emails for the authors, only 11 emails returned an error. we decided not to include the participants corresponding to these emails. it has to be noted that no personal information regarding the individuals included was shared outside the research group, and the information was collected on a secure server and will be discarded 6 months after this research report (containing only aggregated results) has been published. extracted affiliations are organized in sets per paper, i.e., when multiple authors have the same affiliation, they are counted once towards the paper being affiliated with the company.