Hundreds of millions of people now use chatbots every day. And yet the large language models that drive them are so complicated that nobody really understands what they are, how they work, or exactly what they can and can’t do—not even the people who build them. Weird, right? It’s also a problem. Without a clear…
For Frontier Models, this means a shift towards more interpretable architectures and training methods, with an increased focus on security and safety. For Cybersecurity, understanding how LLMs can be exploited and defended against requires increased interpretability.
From an operational perspective, the current black-box nature of LLMs makes it difficult to debug errors, fine-tune performance, and ensure consistent outputs, necessitating heavy reliance on costly and time-consuming trial-and-error methods. Improved interpretability could lead to more targeted model improvements, efficient resource allocation, and robust AI system deployment.