  The most common training pipeline for large language models includes
pretraining, finetuning and aligning phases, with their respective resulting
models, such as the pretrained model and the finetuned model. Finetuned and
aligned models show improved abilities of instruction following and safe
generation, however their abilities to stay factual about the world are
impacted by the finetuning process. Furthermore, the common practice of using
sampling during generation also increases chances of hallucination. In this
work, we introduce a collaborative decoding framework to harness the high
factuality within pretrained models through the concept of critical tokens. We
first design a critical token classifier to decide which model to use for the
next token, and subsequently generates the next token using different decoding
strategies. Experiments with different models and datasets show that our
decoding framework is able to reduce model hallucination significantly,
showcasing the importance of the collaborative decoding framework.
