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On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?

Bender, E., Mcmillan-Major, A., Shmitchell, S., Gebru, T. and Shmitchell, S.-G. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Timnit Gebru * ti****@bl*******.org Black in AI Palo Alto, CA, USA CCS CONCEPTS • Computing Methodologies → Natural Language processing. ACM Reference Format. [online] doi:https://doi.org/10.1145/3442188.3445922

The paper “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” by Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell, presented at FAccT ’21, critiques the trend towards ever-larger language models in NLP research, highlighting ethical, societal, and environmental concerns.

Key Arguments and Contributions: #

  • Environmental and Social Risks: The authors discuss the significant carbon footprint associated with training large models and the potential for these models to perpetuate biases due to uncurated training data scraped from the web.
  • Inequitable Access: The scale of resources required for such models limits their development to well-resourced entities, exacerbating the digital divide and reinforcing the dominance of a few tech giants.
  • Alternatives to Scale: The paper advocates for research that prioritizes value-sensitive design, focusing on fairness, trustworthiness, and climate neutrality, over merely scaling up models.

Critique and Controversy: #

  • The paper’s publication led to a high-profile controversy involving Timnit Gebru’s departure from Google, highlighting the tech industry’s challenges in addressing critical research on AI ethics【†source】.
  • Some critiques argue the paper could offer more concrete solutions for developing ethical and sustainable language models.

Relevance to Prompt Engineering & Architecture: #

  • For prompt engineers and architects, the paper emphasizes the importance of considering the ethical implications of model design from the outset, including careful dataset curation and the potential societal impact of model outputs【11†source】.
  • It challenges professionals to explore innovative approaches that mitigate bias and reduce environmental impact, such as more efficient model architectures or decentralized development processes that engage a broader community.

Conclusion: #

While “On the Dangers of Stochastic Parrots” raises crucial points about the trajectory of AI research, its call to action goes beyond critiquing scale. It invites a fundamental rethinking of priorities in AI development, urging the field to balance innovation with responsibility. The paper serves as a pivotal reference in discussions on ethical AI, emphasizing the need for an industry-wide shift towards more sustainable, equitable, and socially conscious technology development.

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Updated on March 31, 2024