The Limits of Current Evaluation Methods
Evaluating existing AI models is a task we're fairly good at. However, assessing models we're about to build, particularly those venturing into new capabilities, is a different story. Most current evaluation methods assume the next model will be an improved version of the current one.
We're much worse at evaluating the models we're about to build because our benchmarks, safety evaluations, and red-teaming protocols are based on the assumption that the next model is a stronger version of the current one. If it's a different kind of thing, our entire evaluation framework falls apart.
Can We Anticipate the Unforeseen?
Most evaluation methods are designed to test a model's performance on specific tasks or its ability to withstand certain types of attacks. However, these methods are not equipped to handle models that exhibit entirely new capabilities or behaviors. As a result, we may be blindsided by the emergence of new model characteristics.
When a new model breaks away from the mold of its predecessors, our evaluation tools are likely to fail. We're not just talking about a model being more powerful or efficient; we're talking about a fundamental shift in its underlying architecture or functionality.
The question is, can we develop evaluation methods that are more forward-looking and adaptable to new and unexpected model behaviors? The challenge lies in anticipating what we don't know. If we can't foresee the capabilities or characteristics of the next model, how can we design evaluations that will effectively assess it?
Frequently Asked Questions
The consequences of being caught off guard by a new model's capabilities could be significant. If we're unable to evaluate these models effectively, we risk being unprepared for their potential impacts.
What are the main challenges in evaluating new AI models? The main challenge is that our current evaluation methods are based on assumptions about the next model being similar to the current one. How can we improve our evaluation methods? We need to develop more adaptable and forward-looking evaluation methods that can handle new and unexpected model behaviors. What are the potential consequences of failing to evaluate new models effectively? We risk being unprepared for their potential impacts, which could be significant.