GLIMMER: generalized late-interaction memory reranker
Michiel de Jong Yury Zemlyanskiy Nicholas FitzGerald Sumit Sanghai William W. Cohen Joshua Ainslie
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.