Papers by Daeun Kang
Understanding Conversational Implicatures in Humans and LLMs (2026.acl-srw)
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| Challenge: | Large Language Models (LLMs) interpret conversational implicatures using humans as a baseline . et al.: do LLMs exhibit a human-like sensitivity to pragmatic inference? |
| Approach: | They adopt a surprisal-based and response-based metric to measure the accuracy of implicatures . they find that LLMs employing the response- based meter exhibit human-like behavior . |
| Outcome: | The proposed model performs better in the literal condition than in the implied condition . the model differs from humans in its understanding of conversational implicatures . |
Cross-Lingual Suicidal-Oriented Word Embedding toward Suicide Prevention (2020.findings-emnlp)
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| Challenge: | Existing suicide dictionaries for other languages have been limited to Korean . a model that uses social media data to identify whether a post includes suicidal ideation is useful . |
| Approach: | They propose a model that uses existing suicide dictionaries for Korean to predict suicidal ideation . they use the existing dictionary for English and Chinese to translate a post into English and then use the separate suicide-oriented embeddings for English. |
| Outcome: | The proposed model can detect whether a given social media post includes suicidal ideation in Korean . it uses existing suicide dictionaries for other languages to translate the post into English and Chinese, and then embeds the suicide-oriented embeddings for English and China. |