Papers by Qixuan Zhao

2 papers
CLHA: A Simple Yet Effective Contrastive Learning Framework for Human Alignment (2024.lrec-main)

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Challenge: Large language models (LLMs) have attracted considerable attention from academic and industrial communities due to their outstanding performance in various natural language processing tasks.
Approach: They propose a Contrastive Learning Framework for Human Alignment to evaluate the noise within the data and dynamically adjust the training process.
Outcome: The proposed framework surpasses other algorithms in terms of reward model scores, automatic evaluations, and human assessments on the widely used dataset "Helpful and Harmless"
Can MLLMs Understand the Deep Implication Behind Chinese Images? (2025.acl-long)

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Challenge: MLLMs perform poorly on traditional culture images, indicating limitations in understanding high-level semantics and lacking a deep knowledge base of Chinese traditional culture.
Approach: They propose to use Chinese images to assess MLLMs' higher-order perception and understanding of Chinese visual content.
Outcome: The proposed model incorporates images that represent Chinese traditional culture, such as famous Chinese traditional paintings, to ensure the authenticity of the Chinese context.

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