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Hansaem Kim

4 accepted papers

2025

AI Knows Where You Are: Exposure, Bias, and Inference in Multimodal Geolocation with KoreaGEO

EMNLP 2025

Recent advances in vision-language models (VLMs) have enabled accurate image-based geolocation, raising serious concerns about location privacy risks in everyday social media posts. Yet, a systematic evaluation of such risks is still lacking: existing benchmarks show coarse granularity, linguistic b

Cited by 0SourcePDFScholar
2025

Can LLMs Truly Plan? A Comprehensive Evaluation of Planning Capabilities

EMNLP 2025

The existing assessments of planning capabilities of large language models (LLMs) remain largely limited to single-language or specific representation formats. To address this gap, we introduce the Multi-Plan benchmark comprising 204 multilingual and multi-format travel planning scenarios. In experi

Cited by 0SourcePDFScholar
2025

FLUID QA: A Multilingual Benchmark for Figurative Language Usage in Dialogue across English, Chinese, and Korean

EMNLP 2025

Figurative language conveys stance, emotion, and social nuance, making its appropriate use essential in dialogue. While large language models (LLMs) often succeed in recognizing figurative expressions at the sentence level, their ability to use them coherently in conversation remains uncertain. We i

2024

Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean

COLING 2024main

Large language models (LLMs) use pretraining to predict the subsequent word; however, their expansion requires significant computing resources. Numerous big tech companies and research institutes have developed multilingual LLMs (MLLMs) to meet current demands, overlooking less-resourced languages (…