Papers by Junehyoung Kwon
Aligning with Your Own Voice: Self-Corrected Preference Learning for Hallucination Mitigation in LVLMs (2026.findings-acl)
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| Challenge: | Existing preference learning-based approaches rely on proprietary models to construct preference datasets, causing a distributional mismatch between the proprietary and target models. |
| Approach: | They propose a framework that aligns LVLMs using in-distribution data derived from the model's intrinsic knowledge. |
| Outcome: | The proposed framework surpasses baselines in hallucination mitigation while requiring only 5.2k samples. |
Before Forgetting, Learn to Remember: Revisiting Foundational Learning Failures in LVLM Unlearning Benchmarks (2026.findings-acl)
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| Challenge: | Large Vision-Language Models (LVLMs) are capable of learning from vast webscale datasets but pose privacy risks as they can unintentionally memorize sensitive information. |
| Approach: | They propose a Reliable Multi-hop and Multi-image Memorization Benchmark that ensures robust foundational learning through principled data scaling and reasoning-aware QA pairs. |
| Outcome: | Extensive experiments show that ReMem provides a reliable framework for diagnosing both learning and unlearning behaviors in Large Vision-Language Models. |
See-Saw Modality Balance: See Gradient, and Sew Impaired Vision-Language Balance to Mitigate Dominant Modality Bias (2025.naacl-long)
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| Challenge: | Vision-language models often rely on a single modality rather than treating and utilizing them equally, leading to dominance of a specific modality on the overall performance. |
| Approach: | They propose a framework to mitigate dominant modality bias by adjusting the gradient of KL divergence based on each modality's contribution and aligning task directions in a non-conflicting manner. |
| Outcome: | The proposed framework mitigates dominant modality bias on UPMC Food-101, Hateful Memes, and MM-IMDb datasets. |