參數高效調整允許對LLM進行可擴展的個性化調整，以文本輸入為例：縮寫擴展的案例研究。

摘要

縮寫擴展是一種用來加快溝通速度的策略，通過限制輸入量並使用語言模型來建議擴展。在這裡，我們探討了基於先前對話來個性化大型語言模型（LLM）建議的方法，以增強預測的相關性，特別是當用戶數據較少時（約1000個樣本）。具體來說，我們比較了對縮寫輸入進行擴展文本建議的微調、提示微調和檢索增強生成。我們在一位患有ALS的真實用戶身上部署了一個具有80億參數的LLM進行案例研究，並在電影角色個性化方面進行了實驗，結果顯示：（1）在某些情況下可能需要定制化，而提示微調對這些情況有很好的泛化能力；（2）在領域內數據上微調（僅需600個樣本）仍然顯示出一定的增益，但（3）檢索增強的少樣本選擇也優於微調；（4）參數高效調整可實現高效且可擴展的個性化。對於提示微調，我們還發現將學習到的“軟提示”初始化為與用戶相關的概念標記，比隨機初始化具有更高的準確性。

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.

參數高效調整允許對LLM進行可擴展的個性化調整，以文本輸入為例：縮寫擴展的案例研究。

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

摘要

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