– The paper discusses the development and deployment of large language models.
– It explores the risks associated with these models and suggests mitigation strategies.
– The authors recommend considering environmental and financial costs, curating datasets, and exploring research beyond large language models.
The authors take a step back and ask: How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks? They provide recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research directions beyond ever larger language models.
– The development of larger language models has been a trend in NLP.
– The paper discusses the risks associated with big language models.
– Recommendations include considering environmental and financial costs, curating datasets, and exploring research beyond large models.
– The paper discusses the risks and possible mitigation strategies of large language models.
