Papers by Joohyung Yun

2 papers
HELIOS: Harmonizing Early Fusion, Late Fusion, and LLM Reasoning for Multi-Granular Table-Text Retrieval (2025.acl-long)

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Challenge: Existing methods for table-text retrieval are limited due to the need to bridge structured tables and unstructured passages.
Approach: They propose a table-text retrieval system that combines the strengths of both approaches . they propose bipartite subgraph retrieval and query-relevant node expansion .
Outcome: The proposed method outperforms state-of-the-art models with a 42.6% and 39.9% improvement on the OTT-QA benchmark.
LILaC: Late Interacting in Layered Component Graph for Open-domain Multimodal Multihop Retrieval (2025.emnlp-main)

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Challenge: Existing multimodal document retrieval frameworks focus on textual, tabular, and visual elements, but there is a shift toward open-domain multimodal retrieval.
Approach: They propose a multimodal retrieval framework that uses a component graph and a late-interaction-based subgraph retrieval method to capture semantic relationships between components.
Outcome: The proposed framework achieves state-of-the-art retrieval performance on all five benchmarks . it is based on a layered component graph representing multimodal information at two layers .

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