Regression Aware Inference with LLMs
Abstract
Large language models (LLMs) have shown strong results on a range of applications,
including regression and scoring tasks. Typically, one obtains outputs from an LLM via autoregressive sampling from the model’s output distribution. We show that this inference
strategy can be sub-optimal for common regression and scoring evaluation metrics. As a
remedy, we build on prior work on Minimum Bayes Risk decoding, and propose alternate
inference strategies that estimate the Bayes-optimal solution for regression and scoring metrics in closed-form from sampled responses. We show that our proposal significantly improves over baselines across datasets and models.
including regression and scoring tasks. Typically, one obtains outputs from an LLM via autoregressive sampling from the model’s output distribution. We show that this inference
strategy can be sub-optimal for common regression and scoring evaluation metrics. As a
remedy, we build on prior work on Minimum Bayes Risk decoding, and propose alternate
inference strategies that estimate the Bayes-optimal solution for regression and scoring metrics in closed-form from sampled responses. We show that our proposal significantly improves over baselines across datasets and models.
