Decoupled Context Processing for Context Augmented Language Modeling

TL;DR

This paper introduces a decoupled encoder-decoder architecture for language models that effectively incorporates external context, improving efficiency and interpretability while maintaining competitive performance on language modeling and question answering tasks.

Contribution

The paper proposes a simple decoupled architecture for integrating external context into language models, demonstrating its effectiveness and analyzing its behavior and computational implications.

Findings

Achieves competitive results on language modeling and question answering.

Performs grounded context transfer effectively.

Discusses computational benefits of retrieval-augmented models.

Abstract

Language models can be augmented with a context retriever to incorporate knowledge from large external databases. By leveraging retrieved context, the neural network does not have to memorize the massive amount of world knowledge within its internal parameters, leading to better parameter efficiency, interpretability and modularity. In this paper we examined a simple yet effective architecture for incorporating external context into language models based on decoupled Encoder Decoder architecture. We showed that such a simple architecture achieves competitive results on auto-regressive language modeling and open domain question answering tasks. We also analyzed the behavior of the proposed model which performs grounded context transfer. Finally we discussed the computational implications of such retrieval augmented models.