title: Reimagining Retrieval Augmented Language Models for Answering Queries
summary: 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
paper_authors._id: 647d502d83cdb93baf775fb6
paper_authors.name: Wang-Chiew Tan
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/13d66c9f373a144222923002750dc614.svg
paper_authors.fullname: Wang-Chiew Tan
paper_authors.type: user
paper_authors.user: wangchiew
paper_authors._id: 647d502d83cdb93baf775fb7
paper_authors.name: Yuliang Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-07T10:31:47.629Z
paper_authors._id: 631d408a55e6a9f989fa7cc2
paper_authors.avatarUrl: /avatars/6a1c9f39b64081ea5455daa26c51aef4.svg
paper_authors.fullname: Yuliang Li
paper_authors.type: user
paper_authors.user: oi02lyl
paper_authors._id: 647d502d83cdb93baf775fb8
paper_authors.name: Pedro Rodriguez
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-07T10:32:38.139Z
paper_authors._id: 62d5b63605461e9459cefa66
paper_authors.avatarUrl: /avatars/9cbe5d3a5a8f9023e1bbf97fe47d6af3.svg
paper_authors.fullname: Pedro Rodriguez
paper_authors.type: user
paper_authors.user: EntilZha
paper_authors._id: 647d502d83cdb93baf775fb9
paper_authors.name: Richard James
paper_authors._id: 647d502d83cdb93baf775fba
paper_authors.name: Xi Victoria Lin
paper_authors._id: 647d502d83cdb93baf775fbb
paper_authors.name: Alon Halevy
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-07T10:33:32.525Z
paper_authors._id: 636069a08fb9c2420ffe60de
paper_authors.avatarUrl: /avatars/a9f7414d23af040891ac75bff3462814.svg
paper_authors.fullname: Alon Halevy
paper_authors.type: user
paper_authors.user: alonhalevy
paper_authors._id: 647d502d83cdb93baf775fbc
paper_authors.name: Scott Yih
paper_title: Reimagining Retrieval Augmented Language Models for Answering Queries
paper_summary: 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
paper_ai_keywords: semi-parametric retrieval augmented language models model parameters external data sources predictions parametric vanilla large language models views query analyzer/planner provenance question answering