Title: GOOD: Decoding-Time Black-Box LLM Alignment
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Large language models, Alignment, Black-Box, Speculative Decoding

Abstract: Large Language Models (LLMs) have demonstrated immense potential across various applications. However, aligning these models with specific real-world tasks and human preferences typically requires resource-intensive fine-tuning processes such as Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). In this paper, we propose GOOD (Guided Online Optimal Decoding), a novel alignment method that enhances pre-trained models at decoding time without requiring access to their parameters or vocabularies. We observed that different aligned models exhibit similarities in their decisions of alignment-related tokens. Inspired by this, GOOD utilizes a pair of guiding models to identify critical positions related to alignment and adjusts the model’s output dynamically during the decoding phase. Notably, the interaction between the guiding models and the guided model occurs at the string level, enabling GOOD to be applied to align even black-box models with different vocabularies. Experiments show that in weak-to-strong alignment, GOOD can achieve performance comparable to direct fine-tuning in terms of comprehensive capability and harmless generation, reaching relative scores up to 102% and 99% without sacrificing decoding efficiency. Even when guiding across model families, it can recover 98% and 103% of the target performance on the two tasks, respectively. Moreover, GOOD can be applied to enhance already aligned models (improving pass@1 by 52% in code enhancement), making it compatible with various existing alignment techniques.

TL;DR: We propose a decoding-time alignment method that does not require access to model parameters or vocabulary, achieving performance comparable to fine-tuning-based alignment methods while offering faster speed than vanilla decoding.