Papers by Bugeun Kim
Inspecting Soundness of AMR Similarity Metrics in terms of Equivalence and Inequivalence (2024.starsem-1)
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| Challenge: | Existing Abstract Meaning Representation (AMR) similarity metrics have less investigated their soundness . |
| Approach: | They propose a new experimental method to evaluate soundness of AMR similarity metrics in terms of equivalence and inequivalentity. |
| Outcome: | The proposed method satisfies the soundness criteria of existing AMR similarity metrics and improves them by proposing a revised metric, SMATCH . |
DART: An AIGT Detector using AMR of Rephrased Text (2025.naacl-short)
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| Challenge: | Existing methods for detecting AIGTs focus on probabilistic features, causing problems . performance of black-box detectors is low, and it is difficult to detect black- box models . |
| Approach: | They propose a detector that can discriminate multiple black-box LLMs without probabilistic features and the origin of AIGT. |
| Outcome: | The proposed method can discriminate multiple black-box LLMs without probabilistic features and the origin of AIGT. |
EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbers (2022.acl-long)
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| Challenge: | Existing neural approaches to solve algebraic word problems have a plausible answer, but this belief has less been verified due to Q. |
| Approach: | They propose a neural model EPT-X which utilizes natural language explanations to solve an algebraic word problem. |
| Outcome: | The proposed model achieves an average performance of 69.59% on a PEN dataset and produces explanations with quality comparable to human output. |
VoiceBBQ: Investigating Effect of Content and Acoustics in Social Bias of Spoken Language Model (2025.emnlp-main)
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| Challenge: | Due to the nature of speech modality, social bias in Spoken Language Models (SLMs) can emerge from two distinct sources: 1) content aspect and 2) acoustic aspect. |
| Approach: | They propose a dataset that measures social bias by presenting ambiguous or disambiguated contexts followed by questions that may elicit stereotypical responses. |
| Outcome: | The proposed dataset converts every BBQ context into controlled voice conditions, enabling per-axis accuracy, bias, and consistency scores comparable to the original text benchmark. |
Generating Equation by Utilizing Operators : GEO model (2020.coling-main)
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| Challenge: | Existing neural models that use hand-crafted features are expensive and lack domain-specific knowledge. |
| Approach: | They propose a GEO model that uses operator-based features to generate equations using natural language sentences. |
| Outcome: | The proposed model outperforms state-of-the-art models on two datasets and 82.1% in ALG514. |
Point to the Expression: Solving Algebraic Word Problems using the Expression-Pointer Transformer Model (2020.emnlp-main)
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| Challenge: | Existing models that generate solution equations using ‘Op (operator/operand) tokens suffered expression fragmentation and operand-context separation. |
| Approach: | They propose a pure neural model, Expression-Pointer Transformer, which uses (1) ‘Expression’ token and (2) operand-context pointers when generating solution equations. |
| Outcome: | The proposed model achieves comparable performance accuracy to state-of-the-art models and achieves better performance than existing models by at most 40%. |
Belief in Authority: Impact of Authority in Multi-Agent Evaluation Framework (2026.findings-acl)
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| Challenge: | Multi-agent systems utilizing large language models assign authoritative roles to improve performance, yet the impact of authority bias on agent interactions remains underexplored. |
| Approach: | They propose to classify authoritative roles into legitimate, referent, and expert types and analyze their influence across 12-turn conversations using French and Raven’s power-based theory. |
| Outcome: | The proposed model enables agents to perform better in multi-agent evaluations. |
People will agree what I think: Investigating LLM’s False Consensus Effect (2025.findings-naacl)
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| Challenge: | Recent studies have focused on the False Consensus Effect (FCE) where individuals overestimate the extent to which others share their beliefs or behaviors. |
| Approach: | They conduct two studies to examine the FCE phenomenon in Large Language Models (LLMs) they find that popular LLMs have FCE and that they have different prompting styles. |
| Outcome: | The proposed model is popular among LLM users and specifies the conditions when FCE becomes more or less prevalent compared to normal usage. |