Papers by Mingyu Jeon
Selective Test-Time Debiasing for CLIP via Reward Gating (2026.acl-long)
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| Challenge: | Existing methods for debiasing use uniform bias corrections across all input queries . weak debiases retains bias in sensitive queries, while weak dealiases in biased ones . |
| Approach: | They propose a framework that selectively applies debiasing based on input sensitivity . RG-TTA adaptively triggers fairness regularization based upon bias sensitivity of each input . |
| Outcome: | Experiments show that debiasing improves zero-shot performance while maintaining fairness . weak debiased queries distort semantically meaningful information while weak ones fail to mitigate stereotypes . |
See More, Store Less: Memory-Efficient Resolution for Video Moment Retrieval (2026.findings-eacl)
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| Challenge: | Existing video moment retrieval methods rely on sparse frame sampling, risking information loss. |
| Approach: | a new video-based framework enhances memory efficiency while maintaining high information resolution . SMORE uses query-guided captions to encode semantics aligned with user intent . |
| Outcome: | a new framework improves memory efficiency while maintaining high information resolution . it achieves state-of-the-art performance on QVHighlights, Charades-STA, and ActivityNet-Captions benchmarks . |
“Going to a trap house” conveys more fear than “Going to a mall”: Benchmarking Emotion Context Sensitivity for LLMs (2025.findings-emnlp)
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| Challenge: | a new benchmark evaluates whether large language models can understand emotion context sensitivity of humans. |
| Approach: | a new benchmark evaluates whether large language models can understand emotion context sensitivity of humans. |
| Outcome: | a new benchmark evaluates whether large language models can understand emotion context sensitivity of humans. |
Improving Bias Mitigation through Bias Experts in Natural Language Understanding (2023.emnlp-main)
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| Challenge: | Existing approaches to mitigate the detrimental effect of bias on the network include debiasing methods that down-weight the biased examples identified by an auxiliary model, which is trained with explicit bias labels. |
| Approach: | They propose a framework that introduces binary classifiers between the auxiliary model and main model, coined bias experts, to reduce the detrimental effect of bias on the network. |
| Outcome: | The proposed approach outperforms the state-of-the-art on various datasets while achieving high performance on in-distribution data. |