Papers by Seunghyun Hwang

3 papers
PR-MCS: Perturbation Robust Metric for MultiLingual Image Captioning (2023.findings-emnlp)

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Challenge: Existing image captioning metrics are vulnerable to lexical perturbations, but they are not robust to such perturbations.
Approach: They propose a perturbation-robust multilingual CLIPScore which is a reference-free image captioning metric for multiple languages.
Outcome: The proposed metric outperforms baseline metrics in capturing lexical noise of all various perturbation types in all five languages while maintaining a strong correlation with human judgments.
Kiss up, Kick down: Exploring Behavioral Changes in Multi-modal Large Language Models with Assigned Visual Personas (2024.emnlp-main)

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Challenge: Large language models (LLMs) exhibit a high degree of alignment with human behavior based on their robust capabilities for natural language understanding and generation.
Approach: They developed a dataset of 5K fictional avatar images for assignment as visual personas to large language models (LLMs) and analyzed their negotiation behaviors based on the visual traits depicted in these images.
Outcome: The proposed model exhibited aggressive negotiation behaviors when the opponent’s image appeared less aggressive than their own, and less aggressive negotiation behavior when the opposing image appeared more aggressive.
Spatial Dependency Parsing for Semi-Structured Document Information Extraction (2021.findings-acl)

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Challenge: Information extraction (IE) for semistructured document images is often considered as a sequence tagging problem . however, such a setup cannot handle complex spatial relationships and is not suitable for highly structured information.
Approach: They propose a spatial dependency parsing problem that models complex spatial relationships . they evaluate it on receipts, name cards, forms, and invoices and compare it to other methods .
Outcome: The proposed parser achieves similar or better performance on various kinds of documents compared to baselines including BERT-based IOB taggger.

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