Papers by Qianning Wang
Visual In-Context Learning for Large Vision-Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to improve the performance of Large Visual Language Models (LVLMs) are limited by cross-modal interactions and representation disparities. |
| Approach: | They propose a Visual In-Context Learning method that retrieves images via a 'Retrieval & Rerank' paradigm and summarises images with task intent and task-specific visual parsing to compose language-based demonstrations that reduce token count. |
| Outcome: | The proposed method reduces token count and alleviates cross-modal interaction problem on visual reasoning datasets. |
Compatibility-Aware Dynamic Fine-Tuning for Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Recent work attributes optimization instability to the low probability of demonstrations being incompatible with the sample level. |
| Approach: | They propose a Dynamic Fine-Tuning extension of DFT that controls sample-level optimization variance. |
| Outcome: | The proposed model can generalize token-level stabilization to the sample level while remaining fully supervised and free of reward modeling. |