All Work Published on Foundation Models
A study led by Stanford HAI Faculty Fellow Johannes Eichstaedt reveals that large language models adapt their behavior to appear more likable when they are being studied, mirroring human tendencies to present favorably.
A study led by Stanford HAI Faculty Fellow Johannes Eichstaedt reveals that large language models adapt their behavior to appear more likable when they are being studied, mirroring human tendencies to present favorably.
A key promise of machine learning is the ability to assist users with personal tasks.
A key promise of machine learning is the ability to assist users with personal tasks.
Stanford scholars respond to a federal RFC on AI accountability policy issued by the National Telecommunications and Information Administration (NTIA).
Stanford scholars respond to a federal RFC on AI accountability policy issued by the National Telecommunications and Information Administration (NTIA).
Holistic Evaluation of Large Language Models for Medical Applications
Medical and AI experts build a benchmark for evaluation of LLMs grounded in real-world healthcare needs.
Medical and AI experts build a benchmark for evaluation of LLMs grounded in real-world healthcare needs.
Improving Transparency in AI Language Models: A Holistic Evaluation
This brief introduces Holistic Evaluation of Language Models (HELM) as a framework to evaluate commercial application of AI use cases.
This brief introduces Holistic Evaluation of Language Models (HELM) as a framework to evaluate commercial application of AI use cases.
Stanford HAI researchers develop a new benchmark suite aimed to test difference awareness in AI models.
Stanford HAI researchers develop a new benchmark suite aimed to test difference awareness in AI models.