Parameter Efficient Tuning Allows Scalable Personalization of LLMs for
  Text Entry: A Case Study on Abbreviation Expansion
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.
658a48f6cef9b8827b48aa10
Katrin Tomanek
658a48f6cef9b8827b48aa11
Shanqing Cai
658a48f6cef9b8827b48aa12
Subhashini Venugopalan
Parameter Efficient Tuning Allows Scalable Personalization of LLMs for
  Text Entry: A Case Study on Abbreviation Expansion
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.
Personalizing a large language model's suggestions through fine-tuning, prompt-tuning, and retrieval augmentation enhances relevance, with prompt-tuning and retrieval augmentation outperforming fine-tuning in few-shot scenarios.
Large Language Model LLM fine-tuning prompt-tuning retrieval augmented generation parameter efficient tuning soft-prompts concept tokens