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cs.CL2024
PECAN: LLM-Guided Dynamic Progress Control with Attention-Guided Hierarchical Weighted Graph for Long-Document QA
Xinyu Wang, Yanzheng Xiang, Lin Gui +1
Long-document QA presents challenges with large-scale text and long-distance dependencies. Recent advances in Large Language Models (LLMs) enable entire documents to be processed i…
cs.CL2024
Encourage or Inhibit Monosemanticity? Revisit Monosemanticity from a Feature Decorrelation Perspective
Hanqi Yan, Yanzheng Xiang, Guangyi Chen +3
To better interpret the intrinsic mechanism of large language models (LLMs), recent studies focus on monosemanticity on its basic units. A monosemantic neuron is dedicated to a sin…
cs.CL2024★ 1 cited
Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Models
Yanzheng Xiang, Hanqi Yan, Lin Gui +1
In-context learning has become a popular paradigm in natural language processing. However, its performance can be significantly influenced by the order of in-context demonstration…