The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) cover image

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Are Emergent Behaviors in LLMs an Illusion? with Sanmi Koyejo - #671

Feb 12, 2024
01:05:40
Snipd AI
Sanmi Koyejo, assistant professor at Stanford University, discusses his award-winning papers on emergent abilities of large language models (LLMs) and assessing trustworthiness in GPT models. We explore the illusion of LLMs' rapid improvement and the importance of linear metrics. The methodology for evaluating concerns like toxicity and fairness in LLMs is also discussed. Personalized evaluation tests, tracking cross-metrics, and evaluating black box models are additional topics covered.
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Podcast summary created with Snipd AI

Quick takeaways

  • Linear metrics show smooth improvement in model performance, casting doubt on the significance of emergent abilities in large language models.
  • DecodingTrust methodology provides a comprehensive assessment of trustworthiness in GPT models, evaluating concerns like toxicity, privacy, fairness, and robustness.

Deep dives

Research Interests and Papers

Sammy Criagev, an assistant professor at Stanford University, discusses his research agenda focused on trustworthy AI systems. His lab explores foundational aspects, measurement and assessment, as well as mitigation strategies. The lab has recently delved into the study of language models and the emergent properties that arise as these models scale in size.

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