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arXiv · 2503.17482

What's Producible May Not Be Reachable: Measuring the Steerability of Generative Models

Keyon Vafa·Sarah Bentley·Jon Kleinberg·Sendhil Mullainathan
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Abstract

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 decomposition 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 user studies of text-to-image and large language models. Despite the ability of these models to produce high-quality outputs, they all perform poorly on steerability. 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.

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Keyon Vafa, Sarah Bentley, Jon Kleinberg, Sendhil Mullainathan. 2025-03-21. What's Producible May Not Be Reachable: Measuring the Steerability of Generative Models. https://arxiv.org/abs/2503.17482

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