Papers by Ruichuan Chen

2 papers
MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoning (2026.findings-acl)

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Challenge: Existing benchmarks designed to evaluate the reasoning capabilities of large models are limited in scope and lack flexibility to adapt difficulty according to evolving reasoning capacities of models.
Approach: They propose a benchmark that incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.
Outcome: The proposed benchmark incorporates multidisciplinary questions to evaluate the reasoning capabilities of large models and can adjust and update question difficulty based on the reasoning abilities of advanced models.
Defeating Cerberus: Privacy-Leakage Mitigation in Vision Language Models (2026.findings-eacl)

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Challenge: Existing models that process multiple modalities of data have been used for multimodal tasks, but their advanced capabilities raise privacy concerns.
Approach: They propose a method to modify the model’s internal states associated with PII-related content and to reduce the risk of PI I leakage by modifying the model's internal state.
Outcome: The proposed method achieves on average 93.3% refusal rate for various PII-related tasks with minimal impact on unrelated model performances.

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