EP27: On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? by Emily M. Bender, Timnit Gebru and Other

Disclaimer: This podcast is completely AI generated by ⁠⁠⁠⁠NoteBookLM⁠⁠⁠⁠ 🤖 Summary In this episode we talk about this paper, titled “On the Dangers of Stochastic Parrots”, which argues that the increasing size of language models (LMs) in natural language processing (NLP) presents significant risks. The authors express concern regarding the environmental and financial costs of developing and deploying these models, particularly as they disproportionately affect marginalised communities. They also highlight the dangers of using large, uncurated datasets, which tend to overrepresent dominant viewpoints and encode harmful biases. The authors argue that focusing solely on model size and performance on benchmarks misdirects research efforts away from truly understanding language and creating inclusive technologies. They propose a shift towards a more deliberate and ethical approach to NLP research, emphasizing careful planning, data curation, stakeholder engagement, and a focus on mitigating potential harms.

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