Can Language Models Be Too Big? A Discussion with Emily Bender and Margaret Mitchell
Analysis
This article summarizes a podcast episode from Practical AI featuring Emily Bender and Margaret Mitchell, co-authors of the paper "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" The discussion centers on the paper's core arguments, exploring the potential downsides of increasingly large language models. The episode covers the historical context of the paper, the costs (both financial and environmental) associated with training these models, the biases they can perpetuate, and the ethical considerations surrounding their development and deployment. The conversation also touches upon the importance of critical evaluation and pre-mortem analysis in the field of AI.
Key Takeaways
- •Large language models have significant environmental and financial costs.
- •These models can perpetuate biases present in their training data.
- •Ethical considerations and responsible AI development are crucial.
“The episode focuses on the message of the paper itself, discussing the many reasons why the ever-growing datasets and models are not necessarily the direction we should be going.”