Papers by Yuyan Gao

2 papers
SVD-GCL: A Noise-Augmented Hybrid Graph Contrastive Learning Framework for Recommendation (2025.coling-main)

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Challenge: Recent advances in graph neural networks have made it difficult to capture user preferences.
Approach: They propose a graph contrastive learning recommendation model based on noise augmentation that integrates truncated singular value decomposition in the feature engineering stage.
Outcome: The proposed model reduces dimensionality and denoises the original data.
Can LLMs Act as Historians? Evaluating Historical Research Capabilities of LLMs via the Chinese Imperial Examination (2026.acl-long)

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Challenge: Existing benchmarks assess basic knowledge breadth or lexical understanding, failing to capture higher-order skills that are central to historical research.
Approach: They propose a benchmark anchored in the Chinese Imperial Examination system that assesses historical knowledge and lexical understanding.
Outcome: The new benchmark aims to assess the ability of LLMs to process historical materials and documents.

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