25 citations · 34 across the 6 of their papers we have counts for
13 papers · 1 filter
Measuring Political Bias in Large Language Models: What Is Said and How It Is Said
Yejin Bang, Delong Chen, Nayeon Lee +1
We propose to measure political bias in LLMs by analyzing both the content and style of their generated content regarding political issues. Existing benchmarks and measures focus o…
Mitigating Framing Bias with Polarity Minimization Loss
Yejin Bang, Nayeon Lee, Pascale Fung
Framing bias plays a significant role in exacerbating political polarization by distorting the perception of actual events. Media outlets with divergent political stances often use…
Towards Mitigating Hallucination in Large Language Models via Self-Reflection
Ziwei Ji, Tiezheng Yu, Yan Xu +3
Large language models (LLMs) have shown promise for generative and knowledge-intensive tasks including question-answering (QA) tasks. However, the practical deployment still faces…
Survey of Social Bias in Vision-Language Models
Nayeon Lee, Yejin Bang, Holy Lovenia +3
In recent years, the rapid advancement of machine learning (ML) models, particularly transformer-based pre-trained models, has revolutionized Natural Language Processing (NLP) and…
Exploring Cross-Cultural Differences in English Hate Speech Annotations: From Dataset Construction to Analysis
Nayeon Lee, Chani Jung, Junho Myung +4
Warning: this paper contains content that may be offensive or upsetting. Most hate speech datasets neglect the cultural diversity within a single language, resulting in a critical…
KoBBQ: Korean Bias Benchmark for Question Answering
Jiho Jin, Jiseon Kim, Nayeon Lee +3
The Bias Benchmark for Question Answering (BBQ) is designed to evaluate social biases of language models (LMs), but it is not simple to adapt this benchmark to cultural contexts ot…