4 citations · 5 across the 18 of their papers we have counts for
7 papers · 1 filter
Belief in Authority: Impact of Authority in Multi-Agent Evaluation Framework
Junhyuk Choi, Jeongyoun Kwon, Heeju Kim +4
Multi-agent systems utilizing large language models often assign authoritative roles to improve performance, yet the impact of authority bias on agent interactions remains underexp…
Acoustic-based Gender Differentiation in Speech-aware Language Models
Junhyuk Choi, Jihwan Seol, Nayeon Kim +3
Speech-aware Language Models (SpeechLMs) have fundamentally transformed human-AI interaction by enabling voice-based communication, yet they may exhibit acoustic-based gender diffe…
VoiceBBQ: Investigating Effect of Content and Acoustics in Social Bias of Spoken Language Model
Junhyuk Choi, Ro-hoon Oh, Jihwan Seol +1
We introduce VoiceBBQ, a spoken extension of the BBQ (Bias Benchmark for Question Answering) - a dataset that measures social bias by presenting ambiguous or disambiguated contexts…
Pay What LLM Wants: Can LLM Simulate Economics Experiment with 522 Real-human Persona?
Junhyuk Choi, Hyeonchu Park, Haemin Lee +3
Recent advances in Large Language Models (LLMs) have generated significant interest in their capacity to simulate human-like behaviors, yet most studies rely on fictional personas…
PHISH in MESH: Korean Adversarial Phonetic Substitution and Phonetic-Semantic Feature Integration Defense
Byungjun Kim, Minju Kim, Hyeonchu Park +1
As malicious users increasingly employ phonetic substitution to evade hate speech detection, researchers have investigated such strategies. However, two key challenges remain. Firs…
A Stereotype Content Analysis on Color-related Social Bias in Large Vision Language Models
Junhyuk Choi, Minju Kim, Yeseon Hong +1
As large vision language models(LVLMs) rapidly advance, concerns about their potential to learn and generate social biases and stereotypes are increasing. Previous studies on LVLM'…
DART: An AIGT Detector using AMR of Rephrased Text
Hyeonchu Park, Byungjun Kim, Bugeun Kim
As large language models (LLMs) generate more human-like texts, concerns about the side effects of AI-generated texts (AIGT) have grown. So, researchers have developed methods for…