Summary of Matter: Memory-augmented Transformer Using Heterogeneous Knowledge Sources, by Dongkyu Lee et al.
MATTER: Memory-Augmented Transformer Using Heterogeneous Knowledge Sources
by Dongkyu Lee, Chandana Satya Prakash, Jack FitzGerald, Jens Lehmann
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces an efficient memory-augmented transformer called MATTER, designed to retrieve relevant knowledge from multiple heterogeneous knowledge sources for achieving high performance in knowledge-intensive tasks such as question answering. The model retrieves and reads from both unstructured (paragraphs) and semi-structured (QA pairs) sources in the form of fixed-length neural memories. This approach outperforms existing efficient retrieval-augmented models on popular QA benchmarks in terms of accuracy and speed, while achieving competitive results compared to conventional read-and-retrieve models with 100x throughput during inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to make language models better at answering questions by using information from multiple sources. This approach is faster and more accurate than previous methods, and it can use different types of information, such as paragraphs or question-answer pairs. The model is called MATTER, and it’s designed to be fast and efficient while still being very good at answering questions. |
Keywords
» Artificial intelligence » Inference » Question answering » Transformer
