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20232025
most citedZigZagkv: Dynamic KV Cache Compression for Long-context Modeling based on Layer Uncertainty

1 citations · 1 across the 2 of their papers we have counts for

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cs.CL2025

DecEx-RAG: Boosting Agentic Retrieval-Augmented Generation with Decision and Execution Optimization via Process Supervision

Yongqi Leng, Yikun Lei, Xikai Liu +7

Agentic Retrieval-Augmented Generation (Agentic RAG) enhances the processing capability for complex tasks through dynamic retrieval and adaptive workflows. Recent advances (e.g., S…

cs.CL20241 cited

ZigZagkv: Dynamic KV Cache Compression for Long-context Modeling based on Layer Uncertainty

Meizhi Zhong, Xikai Liu, Chen Zhang +5

Large Language models (LLMs) have become a research hotspot. To accelerate the inference of LLMs, storing computed caches in memory has become the standard technique. However, as t…

cs.CL2024

MoDification: Mixture of Depths Made Easy

Chen Zhang, Meizhi Zhong, Qimeng Wang +8

Long-context efficiency has recently become a trending topic in serving large language models (LLMs). And mixture of depths (MoD) is proposed as a perfect fit to bring down both la…

cs.CL2024

Understanding the RoPE Extensions of Long-Context LLMs: An Attention Perspective

Meizhi Zhong, Chen Zhang, Yikun Lei +5

Enabling LLMs to handle lengthy context is currently a research hotspot. Most LLMs are built upon rotary position embedding (RoPE), a popular position encoding method. Therefore, a…

cs.CL2023

Towards the Law of Capacity Gap in Distilling Language Models

Chen Zhang, Qiuchi Li, Dawei Song +3

Language model (LM) distillation aims at distilling the knowledge in a large teacher LM to a small student one. As a critical issue facing LM distillation, a superior student often…