Papers by Jaehyuk Kim
MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation (2025.coling-main)
Copied to clipboard
| Challenge: | Recent advances in large language models have enabled in-context learning (ICL)-based methods to outperform fine-tuning approaches for text-to-SQL tasks. |
| Approach: | They propose a method that leverages multiple prompts to explore a broader search space for possible answers and effectively aggregate them. |
| Outcome: | The proposed method achieves execution accuracies of 65.5% and 89.6% on BIRD and Spider benchmarks. |
Generalizable Prompt Tuning for Audio-Language Models via Semantic Expansion (2026.findings-acl)
Copied to clipboard
| Challenge: | Prompt tuning has achieved remarkable progress in vision–language models, but its generalization ability in ALMs remains underexplored. |
| Approach: | They propose a plug-and-play framework that regularizes the prompt embedding space . they propose introducing a semantic expansion loss with margin constraints that promote compactness . |
| Outcome: | The proposed framework regularizes the prompt embedding space by incorporating semantic neighbors generated by large language models. |
Don’t Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Vision Language Models suffer from hallucinations, attributing incorrect or misleading features to images. |
| Approach: | They propose a test-time approach that recalibrates the influence of blind tokens . they identify blind token by analyzing layer-wise attention distributions over image tokens. |
| Outcome: | The proposed approach reduces hallucinations in large vision language models . it uses a contrastive decoding strategy to balance the influence of blind tokens . |