Papers by Junhyuk Choi
MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories (2021.naacl-main)
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| Challenge: | Existing studies have developed computational models to recognize metaphorical words in sentences. |
| Approach: | They propose a model that leverages contextualized word representation and linguistic metaphor identification theories to detect whether the target word is metaphorical. |
| Outcome: | The proposed model outperforms baseline models on four benchmark datasets . it leverages contextualized word representation and linguistic metaphor identification theories to detect whether the target word is metaphorical. |
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. |
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. |