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Diffbot, a small Silicon Valley company best known for maintaining one of the world’s largest indexes of web knowledge, announced today the release of a new AI model that promises to address one of the biggest challenges in the field: factual accuracy.
The new model, a fine-tuned version of Meta’s LLama 3.3, is the first open-source implementation of a system known as Graph Retrieval-Augmented Generation, or GraphRAG.
Unlike conventional AI models, which rely solely on vast amounts of preloaded training data, Diffbot’s LLM draws on real-time information from the company’s Knowledge Graph, a constantly updated database containing more than a trillion interconnected facts.
“We have a thesis that eventually general purpose reasoning will get distilled down into about 1 billion parameters,” said Mike Tung, Diffbot’s founder and CEO, in an interview with VentureBeat. “You don’t actually want the knowledge in the model. You want the model to be good at just using tools so that it can query knowledge externally.”
How it works
Diffbot’s Knowledge Graph is a sprawling, automated database that has been crawling the public web since 2016. It categorizes web pages into entities such as people, companies, products, and articles, extracting structured information using a combination of computer vision and natural language processing.
Every four to five days, the Knowledge Graph is refreshed with millions of new facts, ensuring it remains up-to-date. Diffbot’s AI model leverages this resource by querying the graph in real time to retrieve information, rather than relying on static knowledge encoded in its training data.
For example, when asked about a recent news event, the model can search the web for the latest updates, extract relevant facts, and cite the original sources. This process is designed to make the system more accurate and transparent than traditional LLMs.
“Imagine asking an AI about the weather,” Tung said. “Instead of generating an answer based on outdated training data, our model queries a live weather service and provides a response grounded in real-time information.”
How Diffbot’s Knowledge Graph beats traditional AI at finding facts
In benchmark tests, Diffbot’s approach appears to be paying off. The company reports its model achieves an 81% accuracy score on FreshQA, a Google-created benchmark for testing real-time factual knowl …