Papers by Eojin Jeon
DIVE: Towards Descriptive and Diverse Visual Commonsense Generation (2023.emnlp-main)
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| Challenge: | Towards human-level visual understanding, visual commonsense generation has been introduced . but current research on visual commonense generation ignores an important human cognitive ability . |
| Approach: | They propose a visual commonsense generation framework to improve inferences by visual common sense generation. |
| Outcome: | The proposed framework outperforms state-of-the-art models in descriptiveness and diversity . human evaluations confirm that the framework aligns closely with human judgments on descriptiveness . |
Break it Down into BTS: Basic, Tiniest Subword Units for Korean (2022.emnlp-main)
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| Challenge: | Existing word embeddings for Korean use the internal structure of words with subword information to improve the quality of word representations. |
| Approach: | They introduce Basic, Tiniest Subword (BTS) units for Korean language that are inspired by Hangeul, the Korean writing system. |
| Outcome: | The proposed framework outperforms the state-of-the-art Korean word embedding by 11.8% on all intrinsic and extrinsic tasks. |
PICTURE: Enhancing Theory-of-Mind in Large Language Models by Revealing, Not Hiding, Characters’ Lack of Knowledge (2026.acl-long)
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| Challenge: | Existing approaches to simulating Theory of Mind (ToM) using LLMs have been a longstanding problem in natural language processing (NLP). |
| Approach: | They propose a new method that enables LLMs to generate a character’s lack of knowledge within free-form Chain-of-Thought (CoT) based on this method, they propose to generate perspective-taking outputs as free- form explanations without event hiding. |
| Outcome: | The proposed method outperforms existing prompting methods by an average of 7.3% on false-belief tasks. |
“Going to a trap house” conveys more fear than “Going to a mall”: Benchmarking Emotion Context Sensitivity for LLMs (2025.findings-emnlp)
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| Challenge: | a new benchmark evaluates whether large language models can understand emotion context sensitivity of humans. |
| Approach: | a new benchmark evaluates whether large language models can understand emotion context sensitivity of humans. |
| Outcome: | a new benchmark evaluates whether large language models can understand emotion context sensitivity of humans. |
Improving Bias Mitigation through Bias Experts in Natural Language Understanding (2023.emnlp-main)
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| Challenge: | Existing approaches to mitigate the detrimental effect of bias on the network include debiasing methods that down-weight the biased examples identified by an auxiliary model, which is trained with explicit bias labels. |
| Approach: | They propose a framework that introduces binary classifiers between the auxiliary model and main model, coined bias experts, to reduce the detrimental effect of bias on the network. |
| Outcome: | The proposed approach outperforms the state-of-the-art on various datasets while achieving high performance on in-distribution data. |