Summary of Llmsteer: Improving Long-context Llm Inference by Steering Attention on Reused Contexts, By Zhuohan Gu et al.
LLMSteer: Improving Long-Context LLM Inference by Steering Attention on Reused Contexts
by Zhuohan Gu, Jiayi Yao, Kuntai Du, Junchen Jiang
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 proposed framework, LLMSteer, aims to improve the performance of large language models (LLMs) while reducing computational costs. By introducing query-independent attention steering, LLMSteer fine-tunes LLMs without requiring additional training data. This approach is tested on popular LLMs and datasets, achieving a 65.9% reduction in the performance gap with baselines and up to 4.8x faster runtime compared to recent attention steering methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are very good at doing some things, but they struggle with understanding longer texts. They also take a long time to work because they use so much computer power. A new way to improve these models is called LLMSteer. It helps the models understand better without needing any extra practice. This works really well and makes the models run faster too. |
Keywords
* Artificial intelligence * Attention
