Papers by Qifei Zhou
Learning to Predict Persona Information for Dialogue Personalization without Explicit Persona Description (2023.findings-acl)
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| Challenge: | Existing approaches to personalize dialogue agents rely on explicit persona descriptions during inference, which severely limits their application in real-world scenarios. |
| Approach: | They propose a method that learns to predict persona information based on the dialogue history to personalize dialogue agents without relying on explicit persona descriptions during inference. |
| Outcome: | The proposed method improves the consistency and engagingness of generated responses when conditioning on the predicted profile of the dialogue agent. |
Enhancing Neural Models with Vulnerability via Adversarial Attack (2020.coling-main)
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| Challenge: | Existing work on adversarial attack to improve performance of NLSM tasks has not been done. |
| Approach: | They propose a general two-stage training framework to enhance neural models with Vulnerability via adversarial attack. |
| Outcome: | The proposed framework improves neural models with Vulnerability via adversarial attack on NLSM datasets. |
Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understanding (2026.acl-long)
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Yuhang Zhou, Mingrui Zhang, Ke Li, Mingyi Wang, Qiao Liu, Qifei Wang, Jiayi Liu, Fei Liu, Serena Li, Weiwei LI, Mingze Gao, Abhishek Kumar, Xiangjun Fan, Zhuokai Zhao, Lizhu Zhang
| Challenge: | Large language models (LLMs) have shown promise on understanding and reasoning over tables, but current approaches remain limited. |
| Approach: | They propose a multi-agent framework that decomposes table reasoning into three specialized roles: planning, coding, and answering. |
| Outcome: | The proposed framework decomposes table reasoning into three specialized roles: planning, coding, and answering. |
Connecting the Dots Between Fact Verification and Fake News Detection (2020.coling-main)
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| Challenge: | Existing methods for detecting fake news rely heavily on supervised learning on a large scale dataset with news articles labeled as fake or real by human experts. |
| Approach: | They propose a simple yet effective approach to connect the dots between fact verification and fake news detection by using a text summarization model pre-trained on news corpora to summarize the long news article into a short claim. |
| Outcome: | The proposed approach enables zero-shot fake news detection, alleviating the need for large scale training data to train fake news detector models. |
Learning from Perturbations: Diverse and Informative Dialogue Generation with Inverse Adversarial Training (2021.acl-long)
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| Challenge: | Inverse Adversarial Training (IAT) algorithm for training neural dialogue systems to avoid generic responses and model dialogue history better. |
| Approach: | They propose an algorithm that encourages the model to be sensitive to perturbations in dialogue history and learn from perturbations. |
| Outcome: | The proposed approach can model dialogue history better and generate more diverse responses on two benchmark datasets. |