Papers by Weitao Jiang
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. |
Stratagem: Learning Transferable Reasoning via Trajectory-Modulated Game Self-Play (2026.acl-long)
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Xiachong Feng, Deyi Yin, Xiaocheng Feng, Yi Jiang, Libo Qin, Yangfan Ye, Lei Huang, Weitao Ma, Qiming Li, Yuxuan Gu, Bing Qin, Lingpeng Kong
| Challenge: | Existing self-play approaches to developing general reasoning in language models rely on terminal game outcomes. |
| Approach: | They propose a game-based reasoning transfer model that addresses two barriers to reasoning transfer. |
| Outcome: | The proposed model improves mathematical reasoning, general reasoning, and code generation benchmarks. |
SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution (2026.findings-acl)
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Xiachong Feng, Yi Jiang, Xiaocheng Feng, Deyi Yin, Libo Qin, Yangfan Ye, Lei Huang, Weitao Ma, Yuxuan Gu, Chonghan Qin, Bing Qin, Lingpeng Kong
| Challenge: | Existing approaches to improve social intelligence of AI systems employ retrospective attributions and lack theoretical grounding. |
| Approach: | They propose a framework that uses Shapley values to ensure fair credit distribution with axiomatic guarantees of efficiency, symmetry, and marginality. |
| Outcome: | The proposed framework matches or exceeds proprietary models including GPT-4o and Claude-3.5-Sonnet. |