LLMSteer: Improving Long-Context LLM Inference by Steering Attention on Reused Contexts

• URL: http://arxiv.org/abs/2411.13009v2
• Date: Thu, 21 Nov 2024 16:49:51 GMT
• Title: LLMSteer: Improving Long-Context LLM Inference by Steering Attention on Reused Contexts
• Authors: Zhuohan Gu, Jiayi Yao, Kuntai Du, Junchen Jiang,
• Abstract summary: We introduce LLMSteer, a fine-tuning-free framework that enhances large language models (LLMs) through query-independent attention steering. Tested on popular LLMs and datasets, LLMSteer narrows the performance gap with baselines by 65.9% and reduces the runtime delay by up to 4.8x.
• Score: 2.0384661785620466
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: As large language models (LLMs) show impressive performance on complex tasks, they still struggle with longer contextual understanding and high computational costs. To balance efficiency and quality, we introduce LLMSteer, a fine-tuning-free framework that enhances LLMs through query-independent attention steering. Tested on popular LLMs and datasets, LLMSteer narrows the performance gap with baselines by 65.9% and reduces the runtime delay by up to 4.8x compared to recent attention steering methods.

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