Papers by Jaeho Han
Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization (2024.emnlp-main)
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| Challenge: | Recent advances in activation quantization methods cause outliers in tokens, causing extra overhead and speedup . a method to quantize per-tensor activation is currently challenging due to the outlier activation outlier. |
| Approach: | They propose a method to find a set of key-value cache which mitigates outliers in subsequent tokens when inserted as a prefix. |
| Outcome: | The proposed method surpasses the established baseline of per-tensor activation quantization and can be seamlessly integrated with the recent activation quantitative method. |
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 . |
RRADistill: Distilling LLMs’ Passage Ranking Ability for Long-Tail Queries Document Re-Ranking on a Search Engine (2024.emnlp-industry)
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Nayoung Choi, Youngjune Lee, Gyu-Hwung Cho, Haeyu Jeong, Jungmin Kong, Saehun Kim, Keunchan Park, Sarah Cho, Inchang Jeong, Gyohee Nam, Sunghoon Han, Wonil Yang, Jaeho Choi
| Challenge: | Large Language Models excel at understanding the semantic relationships between queries and documents, even with lengthy and complex long-tail queries. |
| Approach: | They propose an efficient label generation pipeline and novel sLLM training methods for both encoder and decoder models. |
| Outcome: | The proposed method improves re-ranking for long-tail queries on a Korean-based search platform. |
Language-Grounded Multi-Domain Image Translation via Semantic Difference Guidance (2026.eacl-long)
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| Challenge: | Existing methods for image-to-image translation lack structural integrity and attribute-specific control . Existing approaches lack semantics and provide fine-grained, attribute-based control compared to GAN-based methods . |
| Approach: | They propose a language-grounded attribute-controllable translation framework that grounds semantic differences into corresponding visual transformations while preserving unrelated structural and semantic content. |
| Outcome: | Experiments on CelebA(Dialog) and BDD100K show that LACE achieves high visual fidelity, structural preservation, and interpretable domain-specific control, surpassing baselines. |