重新想像檢索增強語言模型以回答查詢

摘要

我們對大型語言模型進行現實檢驗，並檢視檢索增強語言模型的潛力。這些語言模型是半參數的，模型整合模型參數和來自外部數據源的知識來進行預測，與普通大型語言模型的參數化性質相對。我們提出初步的實驗結果，顯示半參數架構可以透過視圖、查詢分析器/規劃器和出處等方式進行增強，從而打造一個在準確性和效率方面顯著更強大的系統，潛在地適用於問答等其他自然語言處理任務。

English

We present a reality check on large language models and inspect the promise of retrieval augmented language models in comparison. Such language models are semi-parametric, where models integrate model parameters and knowledge from external data sources to make their predictions, as opposed to the parametric nature of vanilla large language models. We give initial experimental findings that semi-parametric architectures can be enhanced with views, a query analyzer/planner, and provenance to make a significantly more powerful system for question answering in terms of accuracy and efficiency, and potentially for other NLP tasks

重新想像檢索增強語言模型以回答查詢

Reimagining Retrieval Augmented Language Models for Answering Queries

摘要

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