Adaptive Decoding via Latent Preference Optimization
Authors: name: Shehzaad Dhuliawala, name: Ilia Kulikov, name: Ping Yu, name: Asli Celikyilmaz, name: Jason Weston, name: Sainbayar Sukhbaatar, name: Jack Lanchantin

During language model decoding, it is known that using higher temperature
sampling gives more creative responses, while lower temperatures are more
factually accurate. However, such models are commonly applied to general
instruction following, which involves both creative and fact seeking tasks,
using a single fixed temperature across all examples and tokens. In this work,
we introduce Adaptive Decoding, a layer added to the model to select the
sampling temperature dynamically at inference time, at either the token or
example level, in order to optimize performance. To learn its parameters we
introduce Latent Preference Optimization (LPO) a general approach to train
discrete latent variables such as choices of temperature. Our method
outperforms all fixed decoding temperatures across a range of tasks that
require different temperatures, including UltraFeedback, Creative Story
Writing, and GSM8K.