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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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.
Understanding Emergent Abilities in Language Models
Criagev explains how their research challenges the notion of emergent abilities in large language models. They conducted experiments using a simplified model and showed that the so-called emergence curve closely resembled the observed behavior of language models. They also highlight the role of metric choice and emphasize the need for careful evaluation and interpretation of model behavior.
Comprehensive Assessment of Trustworthiness
Criagev and his team present their work on decoding trust in GPT models. The goal is to establish evaluations for different aspects of trustworthiness, including toxicity, stereotype bias, robustness, privacy, ethics, and fairness. By providing evaluation methods and a toolbox, they aim to assess and improve trustworthiness in language models.
Challenges in Generalization and Model Tuning
They address the challenges of generalization and model tuning in trustworthiness evaluations. They discuss the tension between models following instructions accurately, potentially leading to unethical behavior, and models that may perform better by ignoring harmful instructions. They also highlight the need for context-specific evaluations in domains like healthcare and education, where specific concerns and ethical standards vary.
Future Directions and Considerations
Criagev discusses the possibility of creating a large suite of evaluations to cover different contexts and specialties. He also emphasizes the importance of metric choice, understanding black box evaluations, and addressing coverage gaps. Furthermore, he mentions the potential for personalized evaluation approaches and the need to examine factors such as training data and model transparency.
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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