Papers by Michiel de Jong
ReadTwice: Reading Very Large Documents with Memories (2021.naacl-main)
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| Challenge: | Existing approaches to model long-range dependencies in text are limited to 512 tokens . however, the amount of compute in attention depends quadratically on the number of tokens in an input text passage. |
| Approach: | They propose a technique that summarises text into a memory table to be used in a second read of the text. |
| Outcome: | The proposed method outperforms models of comparable size on several question answering datasets and sets a new state of the art on the NarrativeQA task, with questions about entire books. |
Generate-and-Retrieve: Use Your Predictions to Improve Retrieval for Semantic Parsing (2022.coling-1)
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Yury Zemlyanskiy, Michiel de Jong, Joshua Ainslie, Panupong Pasupat, Peter Shaw, Linlu Qiu, Sumit Sanghai, Fei Sha
| Challenge: | Existing retrieval techniques for semantic parsing use similarity of query and exemplar inputs . Existing work suggests that appending training samples to training samples improves performance . |
| Approach: | They propose a retrieval procedure that retrieves exemplars for which outputs are similar . existing retrieval techniques are based on similarity of query and exemplar inputs . |
| Outcome: | Existing retrieval techniques rely on similarity of query and exemplar inputs . they retrieve exemplars with similar outputs and generate a final prediction . |
Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering (2023.eacl-main)
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| Challenge: | Existing methods for open-domain question-answering use an open book approach . a recent alternative is to retrieve from a collection of previously-generated question-annwer pairs . |
| Approach: | They propose a new QA system that augments a text-to-text model with a large memory of question-answer pairs and a task for the latent step of question retrieval. |
| Outcome: | The proposed system outperforms closed-book QA and can answer multi-hop questions. |
FiDO: Fusion-in-Decoder optimized for stronger performance and faster inference (2023.findings-acl)
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Michiel de Jong, Yury Zemlyanskiy, Joshua Ainslie, Nicholas FitzGerald, Sumit Sanghai, Fei Sha, William Cohen
| Challenge: | Fusion-in-Decoder (FiD) is a powerful retrieval-augmented language model . however, the architecture used for FiD was not designed for retrieval augmented models . |
| Approach: | They propose to make FiD a modified retrieval-augmented language model with a large decoder and memory bandwidth constraints to alleviate memory bandwidth limitations. |
| Outcome: | The proposed architecture outperforms existing models on knowledge-intensive tasks even on large models on many knowledge-based tasks. |
GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints (2023.emnlp-main)
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| Challenge: | Multi-query attention (MQA) can lead to quality degradation and training instability . it may not be feasible to train separate models optimized for quality and inference. |
| Approach: | They propose a recipe for uptraining existing multi-head language model checkpoints into models with MQA using 5% of original training compute. |
| Outcome: | The proposed model achieves comparable quality to multi-head attention with comparable speed. |
MEMORY-VQ: Compression for Tractable Internet-Scale Memory (2024.naacl-short)
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Yury Zemlyanskiy, Michiel de Jong, Luke Vilnis, Santiago Ontanon, William Cohen, Sumit Sanghai, Joshua Ainslie
| Challenge: | Memory-based methods like LUMEN pre-compute token representations for retrieved passages to speed up inference. |
| Approach: | They propose a method to reduce storage requirements of memory-augmented models . they use a vector quantization variational autoencoder to compress token representations . |
| Outcome: | The proposed method achieves 16x compression rate with comparable performance on KILT benchmark. |
CoLT5: Faster Long-Range Transformers with Conditional Computation (2023.emnlp-main)
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Joshua Ainslie, Tao Lei, Michiel de Jong, Santiago Ontanon, Siddhartha Brahma, Yury Zemlyanskiy, David Uthus, Mandy Guo, James Lee-Thorp, Yi Tay, Yun-Hsuan Sung, Sumit Sanghai
| Challenge: | Many natural language processing tasks require long inputs, but processing long documents with a Transformer model is expensive due to quadratic attention complexity and applying feedforward and attention projection layers to every input token. |
| Approach: | They propose a long-input Transformer model that builds on the intuition that some tokens are more important than others and uses conditional computation to devote more computation to important tokens. |
| Outcome: | The proposed model achieves stronger performance than LongT5 with faster training and inference, achieving SOTA on the long-input SCROLLS benchmark. |