Collaboration or Corporate Capture? Quantifying NLP ’s Reliance on Industry Artifacts and Contributions (2024)

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.

## introduction natural language processing (nlp) has seen rapid growth in recent years, attracting interest from large technology companies. expediting the exploitation of emergent technologies is critical to industry success and this strategy has been applied to nlp, as is evident with the current influx of large language models (llm), chatbots, and evaluation tools. these newfound business opportunities have brought industry to the forefront of nlp research. industry presence in the association of computational linguistics (acl) anthology has correspondingly increased 180% from 2017 to 2022 @xcite . the research community has taken notice: "industry races ahead of academia" was the top takeaway of the 2023 artificial intelligence (ai) index report @xcite and figure 1 : percentage of surveyed emnlp 2022 paper citations with industry affiliation smoothed with a 1d gaussian filter (σ = 2.5). the "expected" line is the percentage of industry affiliation across the entire acl anthology over time. one could expect cited papers of a given year to have the same degree of industry affiliation. the citations are split into the following types: datasets, pre-trained models, prior bests, and full setting scores (see § 3.3 for definitions of citation types). 82% of nlp community survey respondents agreed with the statement "industry will produce the most widely-cited research" @xcite . public investment in ai has failed to keep up with industry: in 2021, google's funding of their subsidiary, deepmind, alone surpassed the entirety of us non-military ai funding @xcite . in parallel to increased industry presence, concerns have surfaced regarding the efficacy of research grounded on general purpose benchmark evaluation. @xcite note that widely-cited natural language understanding (nlu) datasets used to verify general understanding of language models are subject to scope and subjectivity limitations. despite these documented flaws, performance on held-out benchmark test sets is still the de facto standard used to establish publication va-lidity (see § 3.2). the consensus from nlp researchers interviewed in a recent qualitative study @xcite was that "improvements on benchmarks in nlp are the only results that are self-justifying to reviewers". furthermore, benchmark test sets favour institutes with higher computational budgets, since higher scores can sometimes be achieved via sheer computational power @xcite . if substantial computer power is required to achieve state-of-the-art (sota) benchmark performance and thus publish in top venues @xcite , researchers with smaller budgets may find little room for contribution without relying on industry artifacts and contributions. in this work, we define reliance as needing something to a point such that, without it, one could not survive or be successful. reliance enforces necessity which in turn minimizes the possibility of choosing an alternative. instances of reliance have already been identified in nlp. for example, an nlp research community member notes regarding software tools: "you're not gonna just build your own system that's gonna compete on these major benchmarks yourself. you have to start [with] the infrastructure that is there" @xcite . increased reliance on industry artifacts increases the risk of corporate capture -a phenomenon where private industry uses its influence to affect research direction and findings (i.e., science capture), policy (i.e, policy capture), or media coverage (i.e., media capture) @xcite . without provable intent (often obtained through litigation), or a counterfactual, it is exceedingly difficult to prove that 'increased enmeshment of industry and academic research' @xcite is industry capture instead of mutually beneficial collaboration. the purpose of this work is to quantify academia's increased reliance on industry artifacts. since heavy reliance often co-occurs with capture, this is something we believe the field should study further. we note that our work is not proof of industry capture. in this study, we surveyed and collected metadata on 100 emnlp 2022 papers. we found that across types of cited artifacts and contributions, the percentage that are industry affiliated is substantially higher than what would be expected relative to the collective acl anthology (figure 1 , § 4.1). furthermore, the trend of industry affiliation has in-creased in recent years, culminating in the majority of all types of citations we recorded from 2022 being industry affiliated despite only 13.3% of papers published that year having industry affiliation. along with determining the degree of reliance on industry ( § 4.1) we designed our emnlp 2022 survey to address the following additional questions: the remainder of this paper is divided into related works ( § 2), survey methods ( § 3), results from analyzing the survey data ( § 4), and a corresponding discussion ( § 5). the survey and analysis code are available (for research purposes only) at https://github.com/will-c-aitken/ collaboration-or-corporate-captu re . ## analysis some research questions were not immediately answerable from the raw survey data and are addressed in this section via additional analysis. ## discussion industry presence in nlp artifacts and contributions is common and well-above the expected margin. from the perspective of policy research and those who favour research productivity, this relationship may be seen as a purely positive collaboration @xcite . industry has committed substantial financial resources to nlp and the entire research community benefits from more funding and greater professional demand @xcite . it could be argued that the progress achieved in recent years would not have been possible without a high degree of industry involvement. in contrast, those who consider objectivity as an important epistemic virtue of scientific research may be concerned with this trend. @xcite compare the state of ai ethics to the tobacco industry at its peak, a situation which in retrospect is usually cast in a negative light. in a few decades time, will we look back at nlp and ai research with similar collective disdain? will our present efforts be overshadowed by a narrative of objectivity tainted by corporate capture? at the very least, whether for good or bad, publishing in top nlp venues such as emnlp without using industry artifacts and contributions is becoming increasingly infeasible. regardless of viewpoint, all stakeholders relevant to this discussion benefit from increased transparency. throughout our survey we identified areas that our research community could address to assist in quantifying industry presence and reducing barriers to publication. we suggest the following: these suggestions each share the common goal of increasing transparency regarding funding sources and addressing the gap in computing resources between institutions. the compute divide is a well-understood phenomenon and yet it continues to be ignored when submitting and reviewing publications. researchers with less funding are excluded from a subset of problems due to insufficient computing resources. they can only gain access by collaborating with industry, which may not align with their values or abilities foot_3 . our main concern is ensuring that a space always exists for all nlp practitioners to contribute their work. preserving institutional diversity in nlp will strengthen our collective research by ensuring that perspectives from stakeholders without conflicts of interest with industry are still heard. ## conclusion industry has increased investment in ai research and specifically nlp in recent years. our survey of 100 emnlp 2022 papers found that the citation of industry artifacts and contributions is far greater than what would be expected based on yearly industry publication rates. this relationship indicates a reliance on industry. we conclude with a brief discussion highlighting possible positive and negative impacts of industry reliance and provide suggestions for navigating transparency and computing equity issues in nlp.