Reimagining Retrieval Augmented Language Models for Answering Queries

Understand

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