Papers by Zihui Wang

2 papers
SPIDE: Serial and Parallel Intertwined Speculative Decoding (2026.findings-acl)

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Challenge: Speculative decoding (SD) is a training-free SD framework that orchestrates dynamic alternation combining serial dynamic drafting with parallel draft verification.
Approach: They propose a serial and parallel intertwined speculative DEcoding framework that orchestrates dynamic alternation combining serial dynamic drafting and parallel draft verification.
Outcome: The proposed framework accelerates inference while reducing the LLM usage costs.
The Dark Side of Function Calling: Pathways to Jailbreaking Large Language Models (2025.coling-main)

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Challenge: Large language models (LLMs) have remarkable capabilities, but their security implications have been overlooked.
Approach: They propose a “jailbreak function” attack method that exploits alignment discrepancies, user coercion, and the absence of rigorous safety filters.
Outcome: The proposed attack exploits alignment discrepancies, user coercion, and the absence of rigorous safety filters on six state-of-the-art LLMs.

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