Decoupled Context Processing for Context Augmented Language Modeling
This addresses the challenge of parameter efficiency and modularity in language models for AI researchers, though it appears incremental as it builds on existing retrieval-augmented approaches.
The paper tackles the problem of incorporating external knowledge into language models by proposing a decoupled encoder-decoder architecture for context retrieval, achieving competitive results on auto-regressive language modeling and open-domain question answering tasks.
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.