Papers by Tianxiang Yang
VPL: Visual Proxy Learning Framework for Zero-Shot Medical Image Diagnosis (2024.findings-emnlp)
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| Challenge: | Insufficient medical text precision and the modal disparity between text and vision spaces pose challenges for vision-language models like CLIP. |
| Approach: | They propose a visual proxy learning framework that combines a text refinement module and a stable Sinkhorn algorithm to enhance the diagnostic performance. |
| Outcome: | The proposed model outperforms the state-of-the-art CLIP inference by 1.69% to 15.31% on five datasets covering various diseases. |
Large Language Model for Multi-Domain Translation: Benchmarking and Domain CoT Fine-tuning (2024.findings-emnlp)
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| Challenge: | Achieving consistent high-quality machine translation across diverse domains remains a challenge due to limited and imbalanced parallel training data available in various domains. |
| Approach: | They propose a domain Chain of Thought technique that uses the multi-domain intelligence of LLMs to improve translation performance. |
| Outcome: | The proposed method achieves significant improvements in translation accuracy and domain robustness over traditional fine-tuning on a small dataset of four domains. |
Improving Contrastive Learning of Sentence Embeddings from AI Feedback (2023.findings-acl)
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| Challenge: | Existing methods to learn sentence embeddings with rich semantics are limited due to the discrete nature of natural language. |
| Approach: | They propose to use AI feedback to improve contrastive learning of sentence embeddings by combining human feedback and AI feedback. |
| Outcome: | The proposed method achieves state-of-the-art performance on several semantic textual similarity and transfer learning tasks compared to other unsupervised and supervised contrastive learning methods. |
Learnable Privacy Neurons Localization in Language Models (2024.acl-short)
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| Challenge: | Large Language Models (LLMs) memorize and disclose private information, especially Personally Identifiable Information (PII) concerns regarding privacy and security within human society remain poorly understood. |
| Approach: | They propose to use learnable binary weight masks to localize PII-sensitive neurons within LLMs by deactivating localized privacy neurons. |
| Outcome: | The proposed method localizes PII-sensitive neurons across all layers and shows the property of PI I specificity. |
Adaptive Ranking-based Sample Selection for Weakly Supervised Class-imbalanced Text Classification (2022.findings-emnlp)
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| Challenge: | Existing methods to synthesize training labels with labeling rules ignore data imbalance issue . weak supervision paradigm is often used to reduce human efforts to produce training labels inexpensively. |
| Approach: | They propose a model-agnostic framework to alleviate the data imbalance issue in the weak supervision paradigm by combining labeling rules with a probabilistic margin score. |
| Outcome: | The proposed framework outperforms the state-of-the-art imbalanced learning and WS methods on four text classification datasets with four different imbalance ratios. |