Language Model Decoding as Likelihood-Utility Alignment

TL;DR

This paper investigates the alignment between language model likelihoods and task-specific utility, proposing a taxonomy of decoding strategies and providing empirical insights across diverse tasks to guide decoding algorithm selection.

Contribution

It introduces a taxonomy of misalignment mitigation strategies, unifying various decoding algorithms under a common framework and analyzing their applicability across multiple tasks.

Findings

Likelihood-utility misalignment impacts decoding effectiveness

Empirical evidence supports the proposed taxonomy

Guidelines for choosing decoding algorithms across tasks

Abstract

A critical component of a successful language generation pipeline is the decoding algorithm. However, the general principles that should guide the choice of a decoding algorithm remain unclear. Previous works only compare decoding algorithms in narrow scenarios, and their findings do not generalize across tasks. We argue that the misalignment between the model's likelihood and the task-specific notion of utility is the key factor to understanding the effectiveness of decoding algorithms. To structure the discussion, we introduce a taxonomy of misalignment mitigation strategies (MMSs), providing a unifying view of decoding as a tool for alignment. The MMS taxonomy groups decoding algorithms based on their implicit assumptions about likelihood--utility misalignment, yielding general statements about their applicability across tasks. Specifically, by analyzing the correlation between the likelihood and the utility of predictions across a diverse set of tasks, we provide empirical evidence supporting the proposed taxonomy and a set of principles to structure reasoning when choosing a decoding algorithm. Crucially, our analysis is the first to relate likelihood-based decoding algorithms with algorithms that rely on external information, such as value-guided methods and prompting, and covers the most diverse set of tasks to date. Code, data, and models are available at https://github.com/epfl-dlab/understanding-decoding.