Papers by Zelong Yu
Self-Reinforcing Controllable Synthesis of Rare Relational Data via Bayesian Calibration (2026.findings-acl)
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Chongsheng Zhang, Hao Wang, Zelong Yu, Esteban Garces Arias, Julian Rodemann, Zhanshuo Zhang, Qilong Li, Gaojuan Fan, Krikamol Muandet, Christian Heumann
| Challenge: | Existing approaches to synthesis of relational/structured tabular data lack effective feedback mechanism to optimize quality of generated data. |
| Approach: | They propose a relational data generator with dynamic guidance framework that uses chain-of-thought steps to generate tabular data for enhancing downstream imbalanced classification performance. |
| Outcome: | The proposed framework outperforms existing approaches in both data fidelity and downstream imbalanced classification performance on real and synthetic datasets. |
MDS: A Fine-Grained Dataset for Multi-Modal Dialogue Summarization (2024.lrec-main)
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| Challenge: | Summarizing the dialogue into a short message has drawn much attention due to the explosion of various dialogue scenes. |
| Approach: | They develop a multi-modal dialogue summarization dataset to enhance the variety of data available for this research area. |
| Outcome: | The proposed dataset provides a demanding testbed for multi-modal dialogue summarization. |
LlmFixer: Fix the Helpfulness of Defensive Large Language Models (2025.findings-emnlp)
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| Challenge: | Several defense strategies have been introduced to defend against jailbreak attacks, but these strategies weakened the usefulness of large language models. |
| Approach: | They propose a framework that acts on large language models equipped with any defense strategy to recover their usefulness. |
| Outcome: | The proposed framework can be used on large language models to recover their usefulness without updating the parameters of a defensive large language model. |