Summary of Memlong: Memory-augmented Retrieval For Long Text Modeling, by Weijie Liu et al.
MemLong: Memory-Augmented Retrieval for Long Text Modeling
by Weijie Liu, Zecheng Tang, Juntao Li, Kehai Chen, Min Zhang
First submitted to arxiv on: 30 Aug 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 MemLong, a method that enhances the capabilities of long-context language modeling by utilizing an external retriever for historical information retrieval. The approach combines a non-differentiable “ret-mem” module with a partially trainable decoder-only language model and introduces a fine-grained, controllable retrieval attention mechanism that leverages semantic-level relevant chunks. The authors demonstrate that MemLong consistently outperforms other state-of-the-art LLMs on multiple long-context language modeling benchmarks and can extend the context length on a single 3090 GPU from 4k up to 80k. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MemLong is a new way for computers to understand and generate very long pieces of text. Right now, these computers are good at generating short texts like social media posts or news headlines. But they struggle with longer texts that need more information to make sense. This paper introduces a new method called MemLong that helps computers keep track of this extra information and use it to generate longer texts. The results show that MemLong is better than other methods at generating very long texts, and can even handle texts that are 80 times longer than usual. |
Keywords
» Artificial intelligence » Attention » Context length » Decoder » Language model
