What's Producible May Not Be Reachable: Measuring the Steerability of Generative Models
Keyon Vafa, Sarah Bentley, Jon Kleinberg, Sendhil Mullainathan
How should we evaluate the quality of generative models? Many existing metrics focus on a model's producibility, i.e. the quality and breadth of outputs it can generate. However, the actual value from using a generative model stems not just from what it can produce but whether a user with a specific goal can produce an output that satisfies that goal. We refer to this property as steerability. In this paper, we first introduce a mathematical framework for quantifying steerability independently from producibility. Steerability is more challenging to evaluate than producibility because it requires knowing a user's goals. We address this issue by creating a benchmark task that relies on one key idea: sample an output from a generative model and ask users to reproduce it. We implement this benchmark in a large-scale user study of text-to-image models and large language models. Despite the ability of these models to produce high-quality outputs, they all perform poorly on steerability. We also find that steerability can be predicted using machine learning methods, providing insights into model differences. These results suggest that we need to focus on improving the steerability of generative models.  We show such improvements are indeed possible: simple image-based steering mechanisms achieve more than 2x improvement on this benchmark.