Summary of Memory Layers at Scale, by Vincent-pierre Berges et al.
Memory Layers at Scale
by Vincent-Pierre Berges, Barlas Oğuz, Daniel Haziza, Wen-tau Yih, Luke Zettlemoyer, Gargi Ghosh
First submitted to arxiv on: 12 Dec 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 Memory layers utilize trainable key-value lookup mechanisms to add extra parameters without increasing FLOPs. These layers are designed to complement compute-heavy dense feed-forward layers by providing dedicated capacity for storing and retrieving information at a low cost. This paper takes memory layers beyond the proof-of-concept stage, demonstrating their utility at contemporary scales. The results show that language models augmented with improved memory layers outperform dense models with more than twice the computation budget, as well as mixture-of-expert models when matched for both compute and parameters. Gains are particularly pronounced for factual tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Memory layers use a special way to add extra information to a model without using too much computer power. This helps to store and get information quickly. The paper shows that this works well at the same scale as other methods. It finds that language models with improved memory layers do better than other models, even if they have more or less computer power and parameters. This is especially true for tasks like identifying facts. |
