Papers by Liaoyaqi Wang
LIFTED: Multimodal Clinical Trial Outcome Prediction via Large Language Models and Mixture-of-Experts (2025.findings-emnlp)
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| Challenge: | Clinical trials are costly and pivotal processes that require substantial expenses . a new approach to integrate multimodal data for clinical outcome prediction is needed . |
| Approach: | a proposed framework transforms modality-specific data into natural language descriptions . a sparse Mixture-of-Experts mechanism then identifies shared patterns across modalities . |
| Outcome: | a proposed framework outperforms baseline methods in predicting clinical trial outcomes . it transforms modality-specific data into natural language descriptions, encoded via unified encoders . |
GuardEmb: Dynamic Watermark for Safeguarding Large Language Model Embedding Service Against Model Stealing Attack (2024.findings-emnlp)
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| Challenge: | Recent studies reveal the risk of the model stealing attack, posing a financial threat to EaaS providers. |
| Approach: | They propose a dynamic embedding watermarking method that detects watermarks in embedded text . this method is a cross-platform approach that trains a verifier to detect watermark . |
| Outcome: | The proposed method enables an attacker to replicate the proposed method for profit without compromising embedding functionality. |