Stanford debuts first AI benchmark to help understand LLMs

In the world of artificial intelligence (AI) and machine learning (ML), 2022 has arguably been the year of foundation models, or AI models trained on a massive scale. From GPT-3 to DALL-E, from BLOOM to Imagen — another day, it seems, another large language model (LLM) or text-to-image model. But until now, there have been no AI benchmarks to provide a standardized way to evaluate these models, which have developed at a rapidly-accelerated pace over the past couple of years.

LLMs have particularly captivated the AI community, but according to the Stanford Institute for Human-Centered AI (HAI)’s Center for Research on Foundation Models, the absence of an evaluation standard has compromised the community’s ability to understand these models, as well as their capabilities and risks.

To that end, today the CRFM announced the Holistic Evaluation of Language Models (HELM), which it says is the first benchmarking project aimed at improving the transparency of language models and the broader category of foundation models.

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