Papers by Zhaowei Zhu

2 papers
OFFSIDE: Benchmarking Unlearning Misinformation in Multimodal Large Language Models (2026.findings-acl)

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Challenge: Existing benchmarks for MU are limited by a lack of image diversity and coarse-grained unlearning targets.
Approach: They propose a benchmark to evaluate misinformation unlearning in MLLMs . OFFSIDE supports advanced unlearning targets such as fine-grained unlearning and visual rumor removal.
Outcome: OFFSIDE supports advanced unlearning targets, such as fine-grained unlearning and visual rumor removal.
FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)

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Challenge: Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored.
Approach: They propose to use a benchmark to evaluate large language models' financial domain knowledge and practical abilities.
Outcome: The proposed benchmark evaluates large language models' financial domain knowledge and practical abilities.

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