Papers by Eunki Kim
Sightation Counts: Leveraging Sighted User Feedback in Building a BLV-aligned Dataset of Diagram Descriptions (2025.acl-long)
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| Challenge: | Existing studies show that direct generation of diagram descriptions is costly and biased against blind and low-vision (BLV) users. |
| Approach: | They ask sighted individuals to assess diagram descriptions generated by vision-language models . they use latent supervision to guide the models with latent inference . |
| Outcome: | The results show that visual descriptions generated by vision-language models are effective and useful to educators who are themselves BLV and teach visually impaired learners. |
Learning to Insert [PAUSE] Tokens for Better Reasoning (2025.findings-acl)
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| Challenge: | Existing studies have explored incorporating special-purpose tokens into the training process to enhance reasoning capabilities. |
| Approach: | They propose a method for inserting dummy tokens consecutively just before reasoning steps to increase model effectiveness. |
| Outcome: | The proposed method outperforms fine-tuning and previous token insertion methods on multiple datasets and models. |
I0T: Embedding Standardization Method Towards Zero Modality Gap (2025.acl-long)
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| Challenge: | Recent studies on Contrastive Language-Image Pretraining suffer from a *modality gap* . modality gap occurs when image and text embeddings are projected to disparate manifolds . |
| Approach: | They propose a framework that reduces the modality gap by adding two normalization layers to each encoder. |
| Outcome: | The proposed framework reduces the modality gap while preserving the original embedding representations of trained models with their locked parameters. |