title: Parameter Efficient Tuning Allows Scalable Personalization of LLMs for
    Text Entry: A Case Study on Abbreviation Expansion
summary: 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.
paper_authors._id: 658a48f6cef9b8827b48aa10
paper_authors.name: Katrin Tomanek
paper_authors._id: 658a48f6cef9b8827b48aa11
paper_authors.name: Shanqing Cai
paper_authors._id: 658a48f6cef9b8827b48aa12
paper_authors.name: Subhashini Venugopalan
paper_title: Parameter Efficient Tuning Allows Scalable Personalization of LLMs for
    Text Entry: A Case Study on Abbreviation Expansion
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Large Language Model LLM fine-tuning prompt-tuning retrieval augmented generation parameter efficient tuning soft-prompts concept tokens