Papers by Shiliang Xiao

2 papers
PivotAttack: Rethinking the Search Trajectory in Hard-Label Text Attacks via Pivot Words (2026.findings-acl)

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Challenge: Existing hard-label text attacks rely on inefficient "outside-in" strategies that traverse vast search spaces.
Approach: They propose a query-efficient "inside-out" framework that perturbs Pivot Sets to induce label flips.
Outcome: The proposed framework outperforms state-of-the-art methods in both Attack Success Rate and query efficiency.
LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin (2024.acl-long)

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Challenge: Experimental results show that, as the instruction data increases, LoRAMoE can significantly improve the ability to process downstream tasks, while maintaining the world knowledge stored in the LLM.
Approach: They propose a framework that introduces several low-rank adapters and integrates them by using a router network to freeze the backbone model and force a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks.
Outcome: The proposed framework freezes the backbone model and forces a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks, to alleviate world knowledge forgetting.

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