Papers by Wenhao Shao
Leveraging Dual Process Theory in Language Agent Framework for Real-time Simultaneous Human-AI Collaboration (2025.acl-long)
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Shao Zhang, Xihuai Wang, Wenhao Zhang, Chaoran Li, Junru Song, Tingyu Li, Lin Qiu, Xuezhi Cao, Xunliang Cai, Wen Yao, Weinan Zhang, Xinbing Wang, Ying Wen
| Challenge: | Large language models (LLMs) excel in turn-by-turn human-AI collaboration but struggle with simultaneous tasks requiring real-time interaction. |
| Approach: | They propose a language agent framework that integrates *System 1* and *System 2* for efficient real-time simultaneous human-AI collaboration. |
| Outcome: | The proposed framework improves on existing LLM-based agents and human collaborators by integrating Theory of Mind and asynchronous reflection to infer human intentions and perform reasoning-based autonomous decisions. |
Improving Cross-lingual Transfer with Contrastive Negative Learning and Self-training (2024.lrec-main)
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| Challenge: | Recent studies improve cross-lingual transfer learning by better aligning the internal representations within the multilingual model or exploring the information of the target language using self-training. |
| Approach: | They propose to use negative pairs to align the multilingual model and self-train the model to converge on the obtained clean pseudo-labels. |
| Outcome: | The proposed method improves upon the baseline models and can serve as a beneficial complement to the alignment-based methods. |
CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation (2024.lrec-main)
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| Challenge: | Metaphors are a prominent linguistic device in human language and literature, as they add color, imagery, and emphasis to enhance effective communication. |
| Approach: | They propose a large-scale high quality annotated Chinese Metaphor Corpus . they use a set of guidelines to ensure the accuracy and consistency of their annotations . |
| Outcome: | The proposed corpus generates metaphors that resonate more with real-world intuition. |
SA-CLIP: Language Guided Image Spatial and Action Feature Learning (2025.findings-emnlp)
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| Challenge: | Contrastive language-image pretraining models struggle with real-world downstream tasks such as road traffic anomaly detection due to inability to effectively capture spatial and action relationships between objects within images. |
| Approach: | They compile and curate a dataset and train a Spatial and Action relationship aware CLIP model. |
| Outcome: | The proposed model performs well on the traffic anomaly detection task . |