Papers by Jinhyeong Lim
Do Video Language Models really understand the video contexts? (2025.naacl-srw)
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| Challenge: | Recent advances in VideoQA performance have shown that visual language models are effective but the processes of understanding and reasoning in VLMs remain under-explored. |
| Approach: | They propose a framework that incorporates a fine-grained question generation and answering process to measure how well VLMs understand video question answering tasks. |
| Outcome: | The proposed framework incorporates a fine-grained question generation and answering process to measure how well the responses generated by VLMs align with what the model understands. |
Distilling Cross-Modal Knowledge into Domain-Specific Retrievers for Enhanced Industrial Document Understanding (2025.emnlp-industry)
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Jinhyeong Lim, Jeongwan Shin, Seeun Lee, Seongdeok Kim, Joungsu Choi, Jongbae Kim, Chun Hwan Jung, Youjin Kang
| Challenge: | Retrieval-Augmented Generation (RAG) has shown strong performance in open-domain tasks, but its effectiveness in industrial domains is limited by a lack of domain understanding and document structural elements (DSE) such as tables, figures, charts, and formula. |
| Approach: | They propose a knowledge distillation framework that transfers complementary knowledge from Large Language Models and Vision-Language Models into a compact domain-specific retriever. |
| Outcome: | The proposed framework outperforms larger baselines while requiring significantly less computational complexity. |