重塑用于回答查询的检索增强语言模型
Reimagining Retrieval Augmented Language Models for Answering Queries
AI总结：
本文通过现实检验对比普通LLM与检索增强语言模型，提出结合视图、查询分析器/规划器和溯源增强半参数化架构，以提升问答及其他NLP任务的准确率与效率。
摘要：
我们对大型语言模型进行了一次现实检验，并考察了相比之下检索增强语言模型的前景。此类语言模型是半参数化的：模型整合模型参数与来自外部数据源的知识来作出预测，这与普通大型语言模型的参数化本质相反。我们给出的初步实验发现表明，半参数化架构可通过视图、查询分析器/规划器以及溯源得到增强，从而在问答的准确率和效率方面构建一个显著更强大的系统，并可能适用于其他NLP任务。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
