The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) Are Emergent Behaviors in LLMs an Illusion? with Sanmi Koyejo - #671
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Feb 12, 2024 Sanmi Koyejo, an assistant professor at Stanford University, dives into the fascinating world of large language models (LLMs) and their emergent behaviors. He challenges the hype surrounding these models' capabilities, arguing that nonlinear metrics can create illusions of rapid progress. The conversation also discusses his work on trustworthiness in AI, focusing on critical aspects like toxicity and fairness. Sanmi highlights the need for robust evaluation methods as LLMs are integrated into sensitive fields like healthcare and education.
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Emergent Abilities as Measurement Artifacts
- So-called emergent abilities in LLMs might be an illusion due to how they are measured.
- This illusion can stem from using nonlinear metrics showing sudden leaps in abilities instead of the expected smooth improvement from linear metrics.
Creating Artificial Emergence in Autoencoders
- Koyejo's team experimented with autoencoders, models not known for emergent abilities, to demonstrate the impact of metrics.
- By changing the typical continuous metric to a discontinuous one, they created an artificial "emergence" curve, highlighting the metric's influence.
Contextualize LLM Claims with Metric Choice
- Acknowledge the impact of metric choice when discussing LLM abilities.
- Frame claims within the context of the chosen metric to ensure accurate interpretation of results.