  We propose task-adaptive tokenization as a way to adapt the generation
pipeline to the specifics of a downstream task and enhance long-form generation
in mental health. Inspired by insights from cognitive science, our
task-adaptive tokenizer samples variable segmentations from multiple outcomes,
with sampling probabilities optimized based on task-specific data. We introduce
a strategy for building a specialized vocabulary and introduce a vocabulary
merging protocol that allows for the integration of task-specific tokens into
the pre-trained model's tokenization step. Through extensive experiments on
psychological question-answering tasks in both Chinese and English, we find
that our task-adaptive tokenization approach brings a significant improvement
in generation performance while using up to 60% fewer tokens. Preliminary
experiments point to promising results when using our tokenization approach
with very large language models.
