Papers by Changhyeon Kim
OMHBench: Benchmarking Balanced and Grounded Omni-Modal Multi-Hop Reasoning (2026.findings-acl)
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Seunghee Kim, Ingyu Bang, Seokgyu Jang, Changhyeon Kim, Sanghwan Bae, Jihun Choi, Richeng Xuan, Taeuk Kim
| Challenge: | Existing evaluation frameworks for multimodal large language models suffer from limitations . modality shortcuts and biased reasoning paths are common in such models . |
| Approach: | a new benchmark evaluates omni-modal multi-hop reasoning using 6,144 questions . authors propose OMHBench to address these limitations by comparing modalities . |
| Outcome: | OMHBench evaluates omni-modal multi-hop reasoning on 6,144 questions with balanced reasoning paths . evaluation of 13 state-of-the-art models shows performance gap exists between MLLMs and open-source models . |
FCMR: Robust Evaluation of Financial Cross-Modal Multi-Hop Reasoning (2025.acl-long)
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| Challenge: | Existing benchmarks for multimodal large language models lack data contamination and complex queries . financial cross-modal multi-hop reasoning is difficult to evaluate and requires precise cross-module reasoning . |
| Approach: | They propose a benchmark to analyze the reasoning capabilities of multimodal large language models. |
| Outcome: | The proposed model is categorized into three difficulty levels—easy, medium, and hard—for step-by-step evaluation. |
Hyper-CL: Conditioning Sentence Representations with Hypernetworks (2024.acl-long)
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| Challenge: | Existing approaches to sentence embeddings do not capture fine-grained semantics of sentences. |
| Approach: | They propose a method that integrates hypernetworks with contrastive learning to generate conditioned sentence representations. |
| Outcome: | The proposed method narrows the performance gap with the bi-encoder architecture while maintaining the time efficiency characteristic of the tri-encoding approach. |