title: GLIMMER: generalized late-interaction memory reranker
summary: Memory-augmentation is a powerful approach for efficiently incorporating
  external information into language models, but leads to reduced performance
  relative to retrieving text. Recent work introduced LUMEN, a memory-retrieval
  hybrid that partially pre-computes memory and updates memory representations on
  the fly with a smaller live encoder.
    We propose GLIMMER, which improves on this approach through 1) exploiting
  free access to the powerful memory representations by applying a shallow
  reranker on top of memory to drastically improve retrieval quality at low cost,
  and 2) incorporating multi-task training to learn a general and higher quality
  memory and live encoder. GLIMMER achieves strong gains in performance at faster
  speeds compared to LUMEN and FiD on the KILT benchmark of knowledge-intensive
  tasks.
paper_authors._id: 64925bd6a63ac82b347cb34e
paper_authors.name: Michiel de Jong
paper_authors._id: 64925bd6a63ac82b347cb34f
paper_authors.name: Yury Zemlyanskiy
paper_authors._id: 64925bd6a63ac82b347cb350
paper_authors.name: Nicholas FitzGerald
paper_authors._id: 64925bd6a63ac82b347cb351
paper_authors.name: Sumit Sanghai
paper_authors._id: 64925bd6a63ac82b347cb352
paper_authors.name: William W. Cohen
paper_authors._id: 64925bd6a63ac82b347cb353
paper_authors.name: Joshua Ainslie
paper_title: GLIMMER: generalized late-interaction memory reranker
paper_summary: Memory-augmentation is a powerful approach for efficiently incorporating
  external information into language models, but leads to reduced performance
  relative to retrieving text. Recent work introduced LUMEN, a memory-retrieval
  hybrid that partially pre-computes memory and updates memory representations on
  the fly with a smaller live encoder.
    We propose GLIMMER, which improves on this approach through 1) exploiting
  free access to the powerful memory representations by applying a shallow
  reranker on top of memory to drastically improve retrieval quality at low cost,
  and 2) incorporating multi-task training to learn a general and higher quality
  memory and live encoder. GLIMMER achieves strong gains in performance at faster
  speeds compared to LUMEN and FiD on the KILT benchmark of knowledge-intensive
  tasks.
paper_ai_keywords: LUMEN memory-retrieval hybrid shallow reranker multi-task training GLIMMER KILT benchmark knowledge-intensive tasks