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.

GLIMMER 使用浅层 Reranker 和多任务训练来提高知识密集型任务上的检索质量，相对于 LUMEN 和 FiD 获得了较快的速度和强大的性能提升。

GLIMMER：泛化的延迟交互式记忆重排序

GLIMMER: generalized late-interaction memory reranker

The computational complexity of the self-attention mechanism in Transformer
models significantly limits their ability to generalize over long temporal
durations. Memory-augmentation, or the explicit storing of past information in
external memory for subsequent predictions, has become a constructive avenue
for mitigating this limitation. We argue that memory-augmented Transformers can
benefit substantially from considering insights from the memory literature in
humans. We detail an approach for integrating evidence from the human memory
system through the specification of cross-domain linking hypotheses. We then
provide an empirical demonstration to evaluate the use of surprisal as a
linking hypothesis, and further identify the limitations of this approach to
inform future research.

该论文主要探讨利用人类记忆系统中的交叉连接假设来增强记忆增强型 Transformers 模型，并以惊奇度作为交叉连接假设模型进行实证研究，并识别该方法的局限性以指导未来的研究。