Papers by Yuechi Zhou

2 papers
Accurate KV Cache Quantization with Outlier Tokens Tracing (2025.acl-long)

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Challenge: Large Language Models (LLMs) require substantial computational resources during deployment.
Approach: They propose a method to identify outlier tokens and exclude them from quantization . they find that the method can deliver a 6.4 times reduction in memory usage and a 2.5 times increase in throughput .
Outcome: The proposed method delivers a 6.4 times reduction in memory usage and a 2.5 times increase in throughput under 2-bit quantization.
ALW: Adaptive Layer-Wise contrastive decoding enhancing reasoning ability in Large Language Models (2025.findings-acl)

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Challenge: Existing research has demonstrated that contrast decoding of two different models can improve text quality in open-ended text generation but with limited gains on reasoning tasks.
Approach: They propose a framework that dynamically disentangles noise in shallow layers from critical signals in deep layers to enhance reasoning ability.
Outcome: The proposed framework improves answer accuracy while maintaining inference efficiency.

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