Papers by Xingyue Wang
GenProve: Learning to Generate Text with Fine-Grained Provenance (2026.acl-long)
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| Challenge: | Existing methods for large language models (LLMs) are coarse-grained and fail to distinguish between direct quotes and complex reasoning. |
| Approach: | They propose a framework that combines supervised fine-tuning and group relative policy optimization to generate fluent answers while simultaneously producing sentence-level provenance triples. |
| Outcome: | The proposed framework outperforms 14 strong large language models in joint evaluation. |
Do LLMs Understand Wine Descriptors Across Cultures? A Benchmark for Cultural Adaptations of Wine Reviews (2025.findings-emnlp)
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| Challenge: | Recent advances in large language models have opened the door to culture-aware language tasks. |
| Approach: | They propose to integrate regional taste preferences and culture-specific flavor descriptors into wine reviews across Chinese and English. |
| Outcome: | The proposed model incorporates regional taste preferences and culture-specific flavor descriptors into the translation process. |