Summary of Hmt: Hierarchical Memory Transformer For Efficient Long Context Language Processing, by Zifan He et al.
HMT: Hierarchical Memory Transformer for Efficient Long Context Language Processing
by Zifan He, Yingqi Cao, Zongyue Qin, Neha Prakriya, Yizhou Sun, Jason Cong
First submitted to arxiv on: 9 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The Hierarchical Memory Transformer (HMT) is a novel framework that enables large language models to process long contexts by imitating human memorization behavior. Building on previous work, HMT leverages memory-augmented segment-level recurrence and organizes the memory hierarchy by preserving tokens from early input segments and passing memory embeddings along the sequence. This allows for more effective learning and self-adjustment. The authors demonstrate the effectiveness of HMT in general language modeling, question-answering tasks, and summarization tasks, achieving comparable or superior generation quality to long-context LLMs with fewer parameters and less inference memory. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Hierarchical Memory Transformer is a new way for computers to remember things like humans do. It helps big language models understand longer texts by organizing the information they’ve learned before into different levels of importance. This makes it easier for the model to learn and make good decisions. The authors tested this new method on several tasks, like understanding natural language and answering questions, and found that it works better than other methods with fewer resources. |
Keywords
» Artificial intelligence » Inference » Question answering » Summarization » Transformer
