Summary of Intermittent Semi-working Mask: a New Masking Paradigm For Llms, by Mingcong Lu et al.
Intermittent Semi-working Mask: A New Masking Paradigm for LLMs
by Mingcong Lu, Jiangcai Zhu, Wang Hao, Zheng Li, Shusheng Zhang, Kailai Shao, Chao Chen, Nan Li, Feng Wang, Xin Lu
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Intermittent Semi-working Mask (ISM) scheme addresses two challenges in multi-turn dialogues with Large Language Models (LLMs): maintaining high generation quality while reducing latency. The approach leverages a novel masking strategy that alternates between bidirectional and unidirectional attention on queries and answers, combining the strengths of prefix LLMs and causal LLMs. Experimental results demonstrate significant performance gains. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how we can make conversations with Large Language Models better by using a new way to organize information called Intermittent Semi-working Mask (ISM). This helps keep the quality high and makes it faster, which is important for long conversations. The idea is to use both old and new information in a conversation, like looking back at what was said before. |
Keywords
* Artificial intelligence * Attention * Mask