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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Reimagining Retrieval Augmented Language Models for Answering Queries
%A Tan, Wang-Chiew
%A Li, Yuliang
%A Rodriguez, Pedro
%A James, Richard
%A Lin, Xi Victoria
%A Halevy, Alon
%A Yih, Wen-tau
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F tan-etal-2023-reimagining
%X 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.
%R 10.18653/v1/2023.findings-acl.382
%U https://aclanthology.org/2023.findings-acl.382
%U https://doi.org/10.18653/v1/2023.findings-acl.382
%P 6131-6146

Markdown (Informal)

[Reimagining Retrieval Augmented Language Models for Answering Queries](https://aclanthology.org/2023.findings-acl.382) (Tan et al., Findings 2023)

• Reimagining Retrieval Augmented Language Models for Answering Queries (Tan et al., Findings 2023)

ACL

• Wang-Chiew Tan, Yuliang Li, Pedro Rodriguez, Richard James, Xi Victoria Lin, Alon Halevy, and Wen-tau Yih. 2023. Reimagining Retrieval Augmented Language Models for Answering Queries. In Findings of the Association for Computational Linguistics: ACL 2023, pages 6131–6146, Toronto, Canada. Association for Computational Linguistics.