API Is Enough: Conformal Prediction for Large Language Models Without Logit-Access
Jiayuan Su, Jing Luo, Hongwei Wang, Lu Cheng · 2024
This study aims to address the pervasive challenge of quantifying uncertainty in large language models (LLMs) without logit-access.Conformal Prediction (CP), known for its model-agnostic and distribution-free features, is a desired approach for various LLMs and data distributions.However, existing CP methods for LLMs typically assume access to the logits, which are unavailable for some APIonly LLMs.In addition, logits are known to be miscalibrated, potentially leading to degraded CP performance.To tackle these challenges, we introduce a novel CP method that (1) is tailored for API-only LLMs without logitaccess; (2) minimizes the size of prediction sets; and (3) ensures a statistical guarantee of the user-defined coverage.The core idea of this approach is to formulate nonconformity measures using both coarse-grained (i.e., sample frequency) and fine-grained uncertainty notions (e.g., semantic similarity).Experimental results on both close-ended and open-ended Question Answering tasks show our approach can mostly outperform the logit-based CP baselines.