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

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.

01:05:40

Creator website

Episode guests

Sanmi Koyejo

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Highlights

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Episode notes

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.

Context-specific evaluations in domains like healthcare and education are crucial to address specific concerns and ethical standards.

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.

Partial Credit Shortage in Metrics

01:00

The Association Between Model Size and Predictive Accuracy

01:51

Importance of Real-World Relevance in Model Testing

01:16

Models as Systems with Surrounding Components

01:24

Intro

2min

Navigating the Shift from Vision to Language in AI Research

3min

Emergent Abilities in Language Models

25min

Understanding Transition Points in Language Model Performance

4min

Metrics and Emergence in LLMs

5min

Evaluating Trustworthiness in Language Models

16min

Evaluating Language Models in Health and Education

5min

Exploring Evaluation Challenges in Large Language Models

5min

Today we’re joined by Sanmi Koyejo, assistant professor at Stanford University, to continue our NeurIPS 2024 series. In our conversation, Sanmi discusses his two recent award-winning papers. First, we dive into his paper, “Are Emergent Abilities of Large Language Models a Mirage?”. We discuss the different ways LLMs are evaluated and the excitement surrounding their“emergent abilities” such as the ability to perform arithmetic Sanmi describes how evaluating model performance using nonlinear metrics can lead to the illusion that the model is rapidly gaining new capabilities, whereas linear metrics show smooth improvement as expected, casting doubt on the significance of emergence. We continue on to his next paper, “DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models,” discussing the methodology it describes for evaluating concerns such as the toxicity, privacy, fairness, and robustness of LLMs.

The complete show notes for this episode can be found at twimlai.com/go/671.

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

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

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Research Interests and Papers

Understanding Emergent Abilities in Language Models

Comprehensive Assessment of Trustworthiness

Challenges in Generalization and Model Tuning

Future Directions and Considerations

Partial Credit Shortage in Metrics

The Association Between Model Size and Predictive Accuracy

Importance of Real-World Relevance in Model Testing

Models as Systems with Surrounding Components

Remember Everything You Learn from Podcasts