  Abbreviation expansion is a strategy used to speed up communication by
limiting the amount of typing and using a language model to suggest expansions.
Here we look at personalizing a Large Language Model's (LLM) suggestions based
on prior conversations to enhance the relevance of predictions, particularly
when the user data is small (~1000 samples). Specifically, we compare
fine-tuning, prompt-tuning, and retrieval augmented generation of expanded text
suggestions for abbreviated inputs. Our case study with a deployed 8B parameter
LLM on a real user living with ALS, and experiments on movie character
personalization indicates that (1) customization may be necessary in some
scenarios and prompt-tuning generalizes well to those, (2) fine-tuning on
in-domain data (with as few as 600 samples) still shows some gains, however (3)
retrieval augmented few-shot selection also outperforms fine-tuning. (4)
Parameter efficient tuning allows for efficient and scalable personalization.
For prompt-tuning, we also find that initializing the learned "soft-prompts" to
user relevant concept tokens leads to higher accuracy than random
initialization.
