Papers by Yunjian Zhang
Mitigating the Inconsistency Between Word Saliency and Model Confidence with Pathological Contrastive Training (2022.findings-acl)
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| Challenge: | Neural networks are used for various NLP tasks, but their complexity makes them difficult to interpret. |
| Approach: | They propose a framework to mitigate the model pathology and obtain more interpretable models by using contrastive learning and saliency-based samples augmentation to calibrate the sentences representation. |
| Outcome: | The proposed framework can mitigate the model pathology and generate more interpretable models while keeping the model performance. |
Multimodal Fusion with Co-Attention Networks for Fake News Detection (2021.findings-acl)
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| Challenge: | Existing methods to detect fake news with textual and visual contents are ineffective because they concatenate unimodal features without considering inter-modality relations. |
| Approach: | They propose to fuse textual and visual features for fake news detection using multimodal co-attention networks to learn inter-dependencies between multimodal features. |
| Outcome: | Extensive experiments on two realworld datasets show that the proposed network outperforms state-of-the-art methods and learns inter-dependencies among multimodal features. |
Unleashing the Potential of Large Language Models through Spectral Modulation (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, garnering significant attention from both academia and industry. |
| Approach: | They propose to conduct spectral modulation in the parameter space of LLMs to integrate with various models in a plug-and-play manner. |
| Outcome: | The proposed approach improves performance by 10.12% with spectral modulation. |
PARSE: An Efficient Search Method for Black-box Adversarial Text Attacks (2022.coling-1)
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| Challenge: | Neural networks are vulnerable to adversarial examples, i.e., under a black-box scenario. |
| Approach: | They propose a word-level search algorithm that searches for subareas under dynamic search space following the subarea importance. |
| Outcome: | The proposed algorithm can achieve comparable success rates to complex search methods while saving numerous queries and time. |