Papers by Jinseok Kim
LLM Meets Scene Graph: Can Large Language Models Understand and Generate Scene Graphs? A Benchmark and Empirical Study (2025.acl-long)
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Dongil Yang, Minjin Kim, Sunghwan Kim, Beong-woo Kwak, Minjun Park, Jinseok Hong, Woontack Woo, Jinyoung Yeo
| Challenge: | Large language models (LLMs) have demonstrated impressive progress in various text-based tasks, such as question-answering and content generation. |
| Approach: | They propose a benchmark to evaluate Large Language Models’ ability to understand scene graphs and generate them from textual narratives. |
| Outcome: | The proposed model performs well on scene graph understanding but struggles with scene graph generation, particularly for complex narratives. |
AMXFP4: Taming Activation Outliers with Asymmetric Microscaling Floating-Point for 4-bit LLM Inference (2025.findings-acl)
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| Challenge: | Existing research shows unsatisfactory empirical results for microscaling (MX) floating-point (FP) formats. |
| Approach: | They propose a 4-bit asymmetric FP format that handles activation outliers without calibration. |
| Outcome: | The proposed format outperforms MXFP4 by 3% on VQA and rotation-based methods by 1.6% on CSQA. |
LLM-guided Plan and Retrieval: A Strategic Alignment for Interpretable User Satisfaction Estimation in Dialogue (2025.naacl-long)
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| Challenge: | Existing methods for estimating user satisfaction with dialogue systems face challenges due to limited understanding of underlying reasons for user dissatisfaction and high costs of annotating user intentions. |
| Approach: | They propose an interpretable framework for effective user satisfaction prediction . they propose to align utterances with strategies and large language models to retrieve relevant features from utterations. |
| Outcome: | The proposed framework achieves state-of-the-art performance on three benchmarks for the USE task. |