# The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research

arXiv: https://arxiv.org/abs/2305.02797 · HTML: https://mlsanity.com/paper/2305.02797/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 57 references that are in the map.
- First posted on arXiv 4 May 2023.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research](https://mlsanity.com/paper/2010.15581/) (2020)
- [The Grey Hoodie Project: Big Tobacco, Big Tech, and the threat on academic integrity](https://mlsanity.com/paper/2009.13676/) (2020)
- [Examining Citations of Natural Language Processing Literature](https://mlsanity.com/paper/2005.00912/) (2020)
- [Gender Gap in Natural Language Processing Research: Disparities in Authorship and Citations](https://mlsanity.com/paper/2005.00962/) (2020)
- [D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research](https://mlsanity.com/paper/2204.13384/) (2022)
- [A Review of Theory and Practice in Scientometrics](https://mlsanity.com/paper/1501.05462/) (2015)
- [DRIFT: A Toolkit for Diachronic Analysis of Scientific Literature](https://mlsanity.com/paper/2107.01198/) (2021)
- [CS-Insights: A System for Analyzing Computer Science Research](https://mlsanity.com/paper/2210.06878/) (2022)

## Built on by (most cited first, 27 of 27)

- [Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models](https://mlsanity.com/paper/2305.13707/) (2023)
- [Position: Key Claims in LLM Research Have a Long Tail of Footnotes](https://mlsanity.com/paper/2308.07120/) (2023)
- [We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields](https://mlsanity.com/paper/2310.14870/) (2023)
- [Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges](https://mlsanity.com/paper/2405.15604/) (2024)
- [Agreement-Based Cascading for Efficient Inference](https://mlsanity.com/paper/2407.02348/) (2024)
- [Forgotten Knowledge: Examining the Citational Amnesia in NLP](https://mlsanity.com/paper/2305.18554/) (2023)
- [Regulation and NLP (RegNLP): Taming Large Language Models](https://mlsanity.com/paper/2310.05553/) (2023)
- [Citation Amnesia: On The Recency Bias of NLP and Other Academic Fields](https://mlsanity.com/paper/2402.12046/) (2024)
- [To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing](https://mlsanity.com/paper/2310.07715/) (2023)
- [The complementary contributions of academia and industry to AI research](https://mlsanity.com/paper/2401.10268/) (2024)
- [On Tables with Numbers, with Numbers](https://mlsanity.com/paper/2408.06062/) (2024)
- [Building Better: Avoiding Pitfalls in Developing Language Resources when Data is Scarce](https://mlsanity.com/paper/2410.12691/) (2024)
- [Misalignment or misuse? The AGI alignment tradeoff](https://mlsanity.com/paper/2506.03755/) (2025)
- [What Do Indonesians Really Need from Language Technology? A Nationwide Survey](https://mlsanity.com/paper/2506.07506/) (2025)
- [Collaboration or Corporate Capture? Quantifying NLP's Reliance on Industry Artifacts and Contributions](https://mlsanity.com/paper/2312.03912/) (2023)
- [What We Talk About When We Talk About LMs: Implicit Paradigm Shifts and the Ship of Language Models](https://mlsanity.com/paper/2407.01929/) (2024)
- [Reviewer Scores Are Not Comparable Across Research Areas in ML Peer Review](https://mlsanity.com/paper/2607.27209/) (2026)
- [Big AI is accelerating the metacrisis: What can we do?](https://mlsanity.com/paper/2512.24863/) (2025)
- [Quantifying the Knowledge Proximity Between Academic and Industry Research: An Entity and Semantic Perspective](https://mlsanity.com/paper/2602.05211/) (2026)
- [ClaimFlow: Tracing the Evolution of Scientific Claims in NLP](https://mlsanity.com/paper/2603.16073/) (2026)
- [Data Processing for the OpenGPT-X Model Family](https://mlsanity.com/paper/2410.08800/) (2024)
- [Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias](https://mlsanity.com/paper/2512.05714/) (2025)
- [Structural shifts in institutional participation and collaboration within the AI arXiv preprint research ecosystem](https://mlsanity.com/paper/2602.03969/) (2026)
- [Tracing the Evolution of Word Embedding Techniques in Natural Language Processing](https://mlsanity.com/paper/2603.13271/) (2026)
- [LLMs in the Real World: Evaluating "AI" in Emergency Contexts](https://mlsanity.com/paper/2607.00019/) (2026)
- [The Future of NLP may not be at NLP Conferences: Scholarly Migration Patterns in Natural Language Processing](https://mlsanity.com/paper/2607.02416/) (2026)
- [Beyond Good Intentions: When Does the Framing of Multilingual and Low-Resource NLP Research Become a Caricature?](https://mlsanity.com/paper/2608.30866/) (2026)

## Abstract (arXiv)

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.