Papers by Sangho Kim
Thunder-NUBench: A Benchmark for LLMs’ Sentence-Level Negation Understanding (2026.findings-eacl)
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| Challenge: | Negation is a fundamental linguistic phenomenon that poses ongoing challenges for Large Language Models (LLMs) Current benchmarks treat negation as a minor detail within broader tasks, such as natural language inference. |
| Approach: | They propose a novel benchmark specifically created to assess sentence-level understanding of negation in Large Language Models (LLMs). |
| Outcome: | The proposed benchmark compares standard negation with structurally diverse alternatives, such as local negation, contradiction, and paraphrase. |
Thunder-KoNUBench: A Corpus-Aligned Benchmark for Korean Negation Understanding (2026.findings-acl)
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| Challenge: | Negation is a fundamental operation in natural language that reverses the meaning of an expression into its opposite. |
| Approach: | They propose a sentence-level negation understanding benchmark that measures negation performance in Korean. |
| Outcome: | The proposed benchmark improves negation understanding and broader comprehension in Korean. |
Can Language Models Laugh at YouTube Short-form Videos? (2023.emnlp-main)
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| Challenge: | Existing datasets that focus on verbal cues and focus on short-form funny videos focus on focusing on verbs and visual cue. |
| Approach: | They curate a user-generated dataset of 10K multimodal funny videos from YouTube and annotate each video with timestamps and explanations for funny moments. |
| Outcome: | The proposed dataset improves the ability of large language models to understand humor. |