Papers by Guiling Cao

1 papers
Better Red Teaming via Searching with Large Language Model (2025.findings-acl)

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Challenge: Existing methods for evaluating large language models face challenges in managing semantic intricacies and optimizing the efficiency of the search process.
Approach: They propose a framework that reconceptualizes test case generation as a strategic planning problem, leveraging Monte Carlo Tree Search.
Outcome: Experiments on a range of LLM architectures show that the proposed framework achieves state-of-the-art attack success rates without sacrificing computational efficiency.

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