mlsanityData to 14 Oct 2026

GLIMMER: generalized late-interaction memory reranker

arXiv 2306.10231 ↗First posted 17 Jun 2023cs.CL cs.AI cs.LGcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 44 of its 53 references that are in the map. First posted on arXiv 17 Jun 2023.

Builds on

44 of 53 references.

References · 44

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
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32 more
  1. 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,178
  2. 2023Accelerating Large Language Model Decoding with Speculative Samplingcited by 1,006
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  8. 2021ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interactioncited by 476
  9. 2021Large Dual Encoders Are Generalizable Retrieverscited by 456
  10. 2017Zero-Shot Relation Extraction via Reading Comprehensioncited by 426
  11. 2022Confident Adaptive Language Modelingcited by 310
  12. 2022Memorizing Transformerscited by 226
  13. 2019Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoringcited by 165
  14. 2021End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answeringcited by 133
  15. 2023Unlimiformer: Long-Range Transformers with Unlimited Length Inputcited by 128
  16. 2022Training Language Models with Memory Augmentationcited by 113
  17. 2023CoLT5: Faster Long-Range Transformers with Conditional Computationcited by 84
  18. 2021Adaptive Semiparametric Language Modelscited by 83
  19. 2021KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answeringcited by 67
  20. 2022Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformercited by 45
  21. 2020Efficient Document Re-Ranking for Transformers by Precomputing Term Representationscited by 44
  22. 2021Mention Memory: incorporating textual knowledge into Transformers through entity mention attentioncited by 43
  23. 2022An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Taskscited by 36
  24. 2022FiDO: Fusion-in-Decoder optimized for stronger performance and faster inferencecited by 31
  25. 2021Rider: Reader-Guided Passage Reranking for Open-Domain Question Answeringcited by 28
  26. 2022Decoupled Context Processing for Context Augmented Language Modelingcited by 24
  27. 2022Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answeringcited by 19
  28. 2023Pre-computed memory or on-the-fly encoding? A hybrid approach to retrieval augmentation makes the most of your computecited by 17
  29. 2022Multi-Task Retrieval-Augmented Text Generation with Relevance Samplingcited by 10
  30. 2022Can Open-Domain QA Reader Utilize External Knowledge Efficiently like Humans?cited by 10
  31. 2021SDR: Efficient Neural Re-ranking using Succinct Document Representationcited by 7
  32. 2023LAIT: Efficient Multi-Segment Encoding in Transformers with Layer-Adjustable Interactioncited by 7

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 4.

7 papers.

  1. 2024Bridging the Preference Gap between Retrievers and LLMscited by 69
  2. 2023Optimizing Retrieval-augmented Reader Models via Token Eliminationcited by 10
  3. 2023MEMORY-VQ: Compression for Tractable Internet-Scale Memorycited by 2
  4. 2025NAACL2025 Tutorial: Adaptation of Large Language Modelscited by 2
  5. 2025DACL-RAG: Data Augmentation Strategy with Curriculum Learning for Retrieval-Augmented Generationcited by 1
  6. 2025Learning What to Remember: Adaptive Probabilistic Memory Retention for Memory-Efficient Language Modelscited by 1
  7. 2025Beyond RAG vs. Long-Context: Learning Distraction-Aware Retrieval for Efficient Knowledge Groundingcited by 0