参数高效调整允许对LLM进行可扩展的个性化，以文本输入为例：缩写扩展的案例研究

摘要

缩写扩展是一种用于加快通信速度的策略，通过限制键入量并使用语言模型提供建议来扩展缩写。在这里，我们研究了基于先前对话个性化大型语言模型（LLM）建议的方法，以增强预测的相关性，特别是在用户数据较少的情况下（约1000个样本）。具体来说，我们比较了针对缩写输入的扩展文本建议的微调、提示微调和检索增强生成。我们在一个部署的具有8B参数的LLM上进行了案例研究，该模型应用于一位患有ALS的真实用户，并在电影角色个性化方面进行了实验，结果表明：（1）在某些情景下可能需要定制化，提示微调能很好地推广到这些情景；（2）在领域内数据上微调（即使只有600个样本）仍然显示出一定的增益，然而（3）检索增强的少样本选择也优于微调；（4）参数高效调整可实现高效且可扩展的个性化。对于提示微调，我们还发现，将学习的“软提示”初始化为与用户相关的概念标记，比随机初始化能获得更高的准确性。

English

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.

参数高效调整允许对LLM进行可扩展的个性化，以文本输入为例：缩写扩展的案例研究

Parameter Efficient Tuning Allows Scalable Personalization of LLMs for Text Entry: A Case Study on Abbreviation Expansion

摘要

Support