269 citations · 414 across the 29 of their papers we have counts for
11 papers · 1 filter
Functional Interpolation for Relative Positions Improves Long Context Transformers
Shanda Li, Chong You, Guru Guruganesh +7
Preventing the performance decay of Transformers on inputs longer than those used for training has been an important challenge in extending the context length of these models. Thou…
MEMORY-VQ: Compression for Tractable Internet-Scale Memory
Yury Zemlyanskiy, Michiel de Jong, Luke Vilnis +4
Retrieval augmentation is a powerful but expensive method to make language models more knowledgeable about the world. Memory-based methods like LUMEN pre-compute token representati…
GLIMMER: generalized late-interaction memory reranker
Michiel de Jong, Yury Zemlyanskiy, Nicholas FitzGerald +3
Memory-augmentation is a powerful approach for efficiently incorporating external information into language models, but leads to reduced performance relative to retrieving text. Re…
FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction
Chen-Yu Lee, Chun-Liang Li, Hao Zhang +13
The recent advent of self-supervised pre-training techniques has led to a surge in the use of multimodal learning in form document understanding. However, existing approaches that…
WikiWeb2M: A Page-Level Multimodal Wikipedia Dataset
Andrea Burns, Krishna Srinivasan, Joshua Ainslie +5
Webpages have been a rich resource for language and vision-language tasks. Yet only pieces of webpages are kept: image-caption pairs, long text articles, or raw HTML, never all in…
GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
Joshua Ainslie, James Lee-Thorp, Michiel de Jong +3
Multi-query attention (MQA), which only uses a single key-value head, drastically speeds up decoder inference. However, MQA can lead to quality degradation, and moreover it may not…
mLongT5: A Multilingual and Efficient Text-To-Text Transformer for Longer Sequences
David Uthus, Santiago Ontañón, Joshua Ainslie +1
We present our work on developing a multilingual, efficient text-to-text transformer that is suitable for handling long inputs. This model, called mLongT5, builds upon the architec…
A Suite of Generative Tasks for Multi-Level Multimodal Webpage Understanding
Andrea Burns, Krishna Srinivasan, Joshua Ainslie +5
Webpages have been a rich, scalable resource for vision-language and language only tasks. Yet only pieces of webpages are kept in existing datasets: image-caption pairs, long text…
Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference
Tao Lei, Junwen Bai, Siddhartha Brahma +9
We propose Conditional Adapter (CoDA), a parameter-efficient transfer learning method that also improves inference efficiency. CoDA generalizes beyond standard adapter approaches t…
CoLT5: Faster Long-Range Transformers with Conditional Computation
Joshua Ainslie, Tao Lei, Michiel de Jong +9
Many natural language processing tasks benefit from long inputs, but processing long documents with Transformers is expensive -- not only due to quadratic attention complexity but…
Pre-computed memory or on-the-fly encoding? A hybrid approach to retrieval augmentation makes the most of your compute
Michiel de Jong, Yury Zemlyanskiy, Nicholas FitzGerald +4
Retrieval-augmented language models such as Fusion-in-Decoder are powerful, setting the state of the art on a variety of knowledge-intensive tasks. However, they are also expensive…