Papers by Hyeongu Yun
Modality Alignment between Deep Representations for Effective Video-and-Language Learning (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing Video-and-Language models do not take into account the different characteristics of video and text representations. |
| Approach: | They propose a method that exploits Centered Kernel Alignment (CKA) to enhance cross-modality attention by combining multiple modalities. |
| Outcome: | The proposed method outperforms conventional multi-modal methods significantly on video QA tasks with +3.57% accuracy increment compared to the baseline in a popular benchmark dataset. |
PR-MCS: Perturbation Robust Metric for MultiLingual Image Captioning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing image captioning metrics are vulnerable to lexical perturbations, but they are not robust to such perturbations. |
| Approach: | They propose a perturbation-robust multilingual CLIPScore which is a reference-free image captioning metric for multiple languages. |
| Outcome: | The proposed metric outperforms baseline metrics in capturing lexical noise of all various perturbation types in all five languages while maintaining a strong correlation with human judgments. |
Ko-LongRAG: A Korean Long-Context RAG Benchmark Built with a Retrieval-Free Approach (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing benchmarks for long-context RAG focus primarily on English . low-resource languages lack comprehensive evaluation frameworks limiting their progress in retrieval-based tasks. |
| Approach: | Ko-LongRAG is the first Korean long-context RAG benchmark . it adopts a retrieval-free approach designed around Specialized Content Knowledge (SCK) o1 model achieves the highest performance among proprietary models, while EXAONE 3.5 leads among open-sourced models . |
| Outcome: | the benchmark is based on a Korean language model with a retrieval-free approach . o1 model achieves the highest performance among proprietary models, while EXAONE 3.5 leads among open-sourced models. |
MANTA: A Scalable Pipeline for Transmuting Massive Web Corpora into Instruction Datasets (2025.findings-emnlp)
Copied to clipboard
| Challenge: | MANTA-1M generates high-quality large-scale instruction fine-tuning datasets from web corpora . scalability and diversity of the datasets are preserved, allowing expansion into domains requiring intensive knowledge. |
| Approach: | a team of researchers introduce a pipeline that fine-tunes large-scale instruction datasets from web corpora with minimal human intervention. |
| Outcome: | MANTA generates high-quality large-scale instruction fine-tuning datasets from web corpora . leveraging high-performance LLMs, MANTE outperforms other methods in knowledge-intensive tasks . |
ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval (2024.acl-long)
Copied to clipboard
| Challenge: | Existing listwise reranking models rely on pointwise sizing of each passage . Until now, listwise models lack the ability to compare between passages at inference time . |
| Approach: | They propose a listwise reranking approach based on Fusion-in-Decoder that handles multiple candidate passages at train and inference time. |
| Outcome: | The proposed model outperforms the state-of-the-art RankT5 model on the BEIR benchmark for zero-shot retrieval task with a notable +1.3 gain in the average NDCG@10 score. |