Reimagining Retrieval Augmented Language Models for Answering Queries
Wang-Chiew Tan Yuliang Li Pedro Rodriguez Richard James Xi Victoria Lin Alon Halevy Scott Yih
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