Decoupled Context Processing for Context Augmented Language ModelingDownload PDF

Published: 31 Oct 2022, 18:00, Last Modified: 11 Jan 2023, 16:52NeurIPS 2022 AcceptReaders: Everyone
Keywords: Retrieval Augmentation, Encoder-Decoder, Language Modeling, Efficiency
Abstract: Language models can be augmented with 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 $\texttt{Encoder-Decoder}$ 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.
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