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.

翻译：缩写扩展是一种通过限制打字量并借助语言模型提供扩展建议来加速沟通的策略。本研究基于先前对话对大型语言模型（LLM）的建议进行个性化，以提升预测的相关性，尤其针对用户数据量较小（约1000个样本）的场景。具体而言，我们比较了微调、提示调优和检索增强生成三种方法在缩写输入扩展文本建议中的表现。基于部署的80亿参数LLM对一位真实肌萎缩侧索硬化症患者用户的案例研究，以及电影角色个性化实验表明：（1）某些场景下可能需要进行定制化，而提示调优对此类场景具有良好的泛化能力；（2）在领域内数据上进行微调（即使仅使用600个样本）仍能取得一定改进；（3）检索增强的少样本选择方法表现优于微调；（4）参数高效调优可实现高效且可扩展的个性化。对于提示调优，我们还发现将学习的“软提示”初始化为用户相关概念词元，比随机初始化能获得更高准确率。