Papers by Kaixuan Wu
TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Sampling (2025.findings-emnlp)
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Jiahao Qiu, Yifu Lu, Yifan Zeng, Jiacheng Guo, Jiayi Geng, Chenhao Zhu, Xinzhe Juan, Ling Yang, Huazheng Wang, Kaixuan Huang, Yue Wu, Mengdi Wang
| Challenge: | Best-of-N (BoN) sampling generates multiple responses and selects the best one, achieving improved performance but with a high computational cost. |
| Approach: | They propose a framework that integrates a speculative tree-search strategy into Best-of-N (BoN) Sampling. |
| Outcome: | The proposed framework outperforms Best-of-N (BoN) sampling but has high computational cost . tree-search strategy reduces computational overhead while maintaining high output quality . |
Temporal Consistency for LLM Reasoning Process Error Identification (2025.findings-emnlp)
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| Challenge: | Empirical evaluations show consistent performance improvements over baseline methods . 7B/8B distilled models outperform all 70B/72B models and GPT-4o on ProcessBench . |
| Approach: | They propose a temporal consistency method that leverages consistency in a sequence of self-reflection actions to improve verification accuracy. |
| Outcome: | The proposed method outperforms existing methods on three benchmarks . it leverages consistency in a sequence of self-reflection actions to improve accuracy . |
Interpretable Short Video Rumor Detection Based on Modality Tampering (2024.lrec-main)
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| Challenge: | Existing methods to detect rumors from the perspective of modality tampering are labor-intensive and time-consuming. |
| Approach: | They propose a short video rumor detection framework that integrates modality tampering detection and inter-modal matching into a model to detect modality-tampers and interpretability mechanisms to make the results more reasonable. |
| Outcome: | The proposed model improves on the short video rumor dataset by 4.6%-12% compared with other models and can explain whether the short clip is a rumour or not through the perspective of modality tampering. |