Shaping AI Benchmarks with Together AI Co-Founder Percy Liang
May 9, 2024
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Together AI co-founder Percy Liang discusses advancements in AI benchmarking, HELM framework for language models, open-source's role in AI development, challenges of English language bias, and shaping future AI with equitable technologies. Chapters also cover deploying language models, optimizing AI agents, open-source vs closed-source models, and the intersection of music and AI.
HELM framework enhances transparency in AI benchmarks.
Open-source models democratize AI development for inclusivity.
Avoid overfitting by interpreting benchmark results accurately.
Deep dives
Creation of the Helm Evaluation Framework
Percy Liang, an associate professor at Stanford University, co-founded a platform called Together AI focused on generative AI. He detailed the creation of the Helm evaluation framework designed to assess language models holistically. The initiative brought together a collaborative effort within the Stanford Center for Research Foundation models, resulting in a comprehensive evaluation of 30 different models across various metrics and scenarios.
Challenges of Benchmarking Machine Learning Algorithms
Percy Liang discussed the challenges of benchmarking machine learning algorithms, notably addressing the complexity of language models compared to traditional methods. The Helm project aimed to provide a standardized evaluation process by using consistent prompts and methodologies across models and datasets, ensuring a fair comparison. Liang highlighted the importance of reproducibility in benchmarks to uphold transparency and reliability in evaluation results.
Gamification and Overfitting in Benchmarking
The discussion touched upon the gamification of leaderboards and potential overfitting issues in benchmarking. Liang expressed concerns about unintentional model copying and emphasized the need for transparency in benchmark evaluations. The focus was on interpreting benchmark results accurately to avoid overfitting to specific tasks, addressing the challenges of data set selection and model optimization.
Future of AI Models and Agent Benchmarks
The conversation delved into the evolving landscape of AI models and the implications for benchmarking agents. Liang highlighted the need for scalable and secure agent systems trained on open models. The dialogue underscored the importance of community collaboration and transparency in developing AI models. Liang also emphasized the potential for advancements in agent-based systems to improve ML engineering tasks and applications.
Intersection of Music and AI in Percy Liang's Work
Percy Liang's background as a classical pianist intersects with his AI research, notably in the field of music generation. Working with researchers to develop anticipatory music models, he emphasized controllable generation for musicians. This innovative approach aims to empower musicians to edit and refine generated music, bridging the gap between AI and musical creativity. Liang's exploration of music and AI showcases a blend of personal passion and technological advancement in the realm of music production.
In this episode of Gradient Dissent, Together AI co-founder and Stanford Associate Professor Percy Liang joins host, Lukas Biewald, to discuss advancements in AI benchmarking and the pivotal role that open-source plays in AI development.
He shares his development of HELM—a robust framework for evaluating language models. The discussion highlights how this framework improves transparency and effectiveness in AI benchmarks. Additionally, Percy shares insights on the pivotal role of open-source models in democratizing AI development and addresses the challenges of English language bias in global AI applications. This episode offers in-depth insights into how benchmarks are shaping the future of AI, highlighting both technological advancements and the push for more equitable and inclusive technologies.
