Reproduce “Conformal Language Modeling” on OpenResearch

About this paper

Victor Quach, Adam Fisch, Tal Schuster, Adam Yala, Jae Ho Sohn, Tommi S. Jaakkola, Regina Barzilay

Researchers from CSAIL, MIT, Google Research, UC Berkeley, and UCSF developed a framework that extends conformal prediction to generative large language models, providing distribution-free statistical guarantees for prediction sets containing acceptable responses. This approach enables LMs to identify trustworthy output components and ensures that prediction sets include at least one correct response with a user-specified probability, thereby enhancing reliability for high-stakes applications.

Key insights

• Conformal prediction can be adapted to the unbounded output spaces of generative LMs through a calibrated, sampling-based procedure that effectively manages sampling, rejection, and stopping rules.
• Leveraging LM-internal likelihood scores for quality estimation, alongside a calibrated diversity metric, is crucial for constructing prediction sets that are both statistically valid and computationally efficient.
• Providing fine-grained uncertainty estimates by identifying individually confident components within LM-generated text significantly enhances the practical utility and interpretability of outputs, especially for verbose or partially incorrect responses.

Summary by alphaXiv.