Papers by Shaoyuan Xu
Text-to-Distribution Prediction with Quantile Tokens and Neighbor Context (2026.acl-long)
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Yilun Zhu, Yuan Zhuang, Nikhita Vedula, Dushyanta Dhyani, Shaoyuan Xu, Mohsen Bayati, Bryan Wang, Shervin Malmasi
| Challenge: | Existing methods for text regression lack local grounding and rely on shared representations. |
| Approach: | They propose a distributional regression model with quantile tokens that insert dedicated quantiles into the input sequence. |
| Outcome: | The proposed method outperforms baseline models on the inside Airbnb and StackSample datasets. |
KG-FLIP: Knowledge-guided Fashion-domain Language-Image Pre-training for E-commerce (2023.acl-industry)
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| Challenge: | Various visionlanguage pre-training (VLP) models learn cross-modal alignment from large-scale well-aligned image-text datasets without leveraging external knowledge. |
| Approach: | They propose a knowledge-guided fashion-domain language-image pre-training framework that learns fine-grained representations in e-commerce domain and utilizes external knowledge to improve the pre-train efficiency. |
| Outcome: | The proposed framework outperforms state-of-the-art models on Amazon and Fashion-Gen datasets by large margins. |
Q-Tuning: Queue-based Prompt Tuning for Lifelong Few-shot Language Learning (2024.findings-naacl)
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| Challenge: | Existing methods for continual prompt tuning are limited by the ever-growing parameter scale of modern language models (e.g., GPT-4 that may have 1.76 trillion parameters). |
| Approach: | They propose a method for continual prompt tuning that enables the lifelong learning of a pre-trained language model by adding a task-specific prompt to a queue of older tasks. |
| Outcome: | The proposed method outperforms the state-of-the-art methods substantially on continual prompt tuning benchmarks. |