Papers by Jiaying Hu
Learning to Contrast the Counterfactual Samples for Robust Visual Question Answering (2020.emnlp-main)
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| Challenge: | Existing methods of generating counterfactual samples are not fully utilized in the task of Visual Question Answering (VQA). |
| Approach: | They propose a self-supervised contrastive learning mechanism to learn the relationship between original samples, factual samples and counterfactual samples. |
| Outcome: | The proposed method surpasses state-of-the-art models on the VQA-CP dataset, a diagnostic benchmark for assessing the VQ model’s robustness. |
SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing (2020.acl-main)
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| Challenge: | Existing models with excessive information are inefficient and costly . |
| Approach: | They propose to integrate a Dialogue State Tracker with Slot Attention and Slot Information Sharing to reduce redundant information’s interference and improve long dialogue context tracking. |
| Outcome: | The proposed model significantly outperforms existing models on the MultiWOZ dataset. |
Rewarding the Rare: Uniqueness-Aware RL for Creative Problem Solving in LLMs (2026.findings-acl)
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Zhiyuan Hu, Yucheng Wang, Yufei He, Jiaying Wu, Yilun Zhao, See-Kiong Ng, Cynthia Breazeal, Anh Tuan Luu, Hae Won Park, Bryan Hooi
| Challenge: | Reinforcement learning (RL) is a paradigm for post-training large language models, but it suffers from exploration collapse . a new study finds that RL fails to reward correct solutions that exhibit rare high-level strategies . |
| Approach: | They propose a method that rewards correct solutions that exhibit rare high-level strategies by clustering rollouts according to their high- level solution strategies. |
| Outcome: | The proposed approach improves pass@k across large sampling budgets and increases area under the pass@K curve (AUC@K) without sacrificing pass@1. |