Papers by Huaizhi Qu

2 papers
Bit-Flip Error Resilience in LLMs: A Comprehensive Analysis and Defense Framework (2025.emnlp-main)

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Challenge: Bit-flip errors (BFEs) are hardware faults where individual bits in memory or processing units are unintentionally flipped.
Approach: They propose a novel defense strategy to mitigate bit-flip errors (BFEs) they propose bfe protection and a self-correction mechanism to minimize performance degradation .
Outcome: The proposed defense strategy minimizes performance degradation while significantly improving robustness against BFEs.
UQ-Merge: Uncertainty Guided Multimodal Large Language Model Merging (2025.findings-acl)

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Challenge: Existing models merging methods often lead to suboptimal performance due to harmful models . et al., 2018; 59: 59-64.
Approach: They propose an uncertainty-guided MLLM merging algorithm that integrates models into a single MLML.
Outcome: The proposed algorithm improves on held-in and held-out vision-language benchmarks.

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