Papers by Kangsan Kim
UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities (2026.acl-long)
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| Challenge: | Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external knowledge relevant to queries. |
| Approach: | They propose a framework to retrieve and integrate knowledge from heterogeneous sources with diverse modalities and granularities. |
| Outcome: | The proposed framework shows superiority over existing methods on 10 benchmarks of multiple modalities. |
VideoRAG: Retrieval-Augmented Generation over Video Corpus (2025.findings-acl)
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| Challenge: | Existing approaches to generating models rely on text and images, but video content is a rich source of multimodal knowledge. |
| Approach: | They propose a framework that dynamically retrieves videos based on their relevance with queries . they use large video language models to represent video content for retrieval . |
| Outcome: | The proposed framework retrieves videos based on relevance with queries and integrates both visual and textual information. |
BlendX: Complex Multi-Intent Detection with Blended Patterns (2024.lrec-main)
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| Challenge: | Existing datasets such as MixATIS and MixSNIPS have limitations in their formulation. |
| Approach: | They propose a set of multi-intent detection datasets that feature more diverse patterns than their predecessors. |
| Outcome: | The proposed datasets feature more diverse patterns than their predecessors and are more complex and diverse than existing datasets. |