Summary of Associative Recurrent Memory Transformer, by Ivan Rodkin et al.
Associative Recurrent Memory Transformer
by Ivan Rodkin, Yuri Kuratov, Aydar Bulatov, Mikhail Burtsev
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper presents Associative Recurrent Memory Transformer (ARMT), a novel neural architecture designed to process very long sequences efficiently. ARMT combines transformer self-attention for local context and segment-level recurrence for storing task-specific information across the sequence. Experimental results show that ARMT outperforms existing alternatives in associative retrieval tasks, achieving a new state-of-the-art performance record on the BABILong multi-task long-context benchmark. Specifically, ARMT achieves an accuracy of 79.9% when answering single-fact questions over 50 million tokens. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special computer model that can understand and process very long pieces of text. The model is called Associative Recurrent Memory Transformer (ARMT). It’s like a super powerful brain that can store and recall lots of information from the text. The researchers tested this model and found it was much better than other models at answering questions about specific facts in a very long piece of text. |
Keywords
* Artificial intelligence * Multi task * Recall * Self attention * Transformer
