FACTS Benchmark Suite: Systematically evaluating the factuality of large language models

Large language models (LLMs) are increasingly becoming a primary source for information delivery across diverse use cases, so it’s important that their responses are factually accurate.

In order to continue improving their performance on this industry-wide challenge, we have to better understand the types of use cases where models struggle to provide an accurate response and better measure factuality performance in those areas.

The FACTS Benchmark Suite

Today, we’re teaming up with Kaggle to introduce the FACTS Benchmark Suite. It extends our previous work developing the FACTS Grounding Benchmark, with three additional factuality benchmarks, including:

• A Parametric Benchmark that measures the model’s ability to access its internal knowledge accurately in factoid question use-cases.
• A Search Benchmark that tests a model’s ability to use Search as a tool to retrieve information and synthesize it correctly.
• A Multimodal Benchmark that tests a model’s ability to answer prompts ...