4.5k citations · 5.6k across the 18 of their papers we have counts for
21 papers
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…
A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity
Yejin Bang, Samuel Cahyawijaya, Nayeon Lee +10
This paper proposes a framework for quantitatively evaluating interactive LLMs such as ChatGPT using publicly available data sets. We carry out an extensive technical evaluation of…
RHO (): Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding
Ziwei Ji, Zihan Liu, Nayeon Lee +4
Dialogue systems can leverage large pre-trained language models and knowledge to generate fluent and informative responses. However, these models are still prone to produce halluci…
Evaluating Parameter Efficient Learning for Generation
Peng Xu, Mostofa Patwary, Shrimai Prabhumoye +6
Parameter efficient learning methods (PERMs) have recently gained significant attention as they provide an efficient way for pre-trained language models (PLMs) to adapt to a downst…
Factuality Enhanced Language Models for Open-Ended Text Generation
Nayeon Lee, Wei Ping, Peng Xu +4
Pretrained language models (LMs) are susceptible to generate text with nonfactual information. In this work, we measure and improve the factual accuracy of large-scale LMs for open…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias
Nayeon Lee, Yejin Bang, Tiezheng Yu +2
Media news framing bias can increase political polarization and undermine civil society. The need for automatic mitigation methods is therefore growing. We propose a new task, a ne…
Towards Answering Open-ended Ethical Quandary Questions
Yejin Bang, Nayeon Lee, Tiezheng Yu +10
Considerable advancements have been made in various NLP tasks based on the impressive power of large language models (LLMs) and many NLP applications are deployed in our daily live…
Survey of Hallucination in Natural Language Generation
Ziwei Ji, Nayeon Lee, Rita Frieske +10
Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based…
Assessing Political Prudence of Open-domain Chatbots
Yejin Bang, Nayeon Lee, Etsuko Ishii +2
Politically sensitive topics are still a challenge for open-domain chatbots. However, dealing with politically sensitive content in a responsible, non-partisan, and safe behavior w…
Dynamically Addressing Unseen Rumor via Continual Learning
Nayeon Lee, Andrea Madotto, Yejin Bang +1
Rumors are often associated with newly emerging events, thus, an ability to deal with unseen rumors is crucial for a rumor veracity classification model. Previous works address thi…
On Unifying Misinformation Detection
Nayeon Lee, Belinda Z. Li, Sinong Wang +4
In this paper, we introduce UnifiedM2, a general-purpose misinformation model that jointly models multiple domains of misinformation with a single, unified setup. The model is trai…
Mitigating Media Bias through Neutral Article Generation
Nayeon Lee, Yejin Bang, Andrea Madotto +1
Media bias can lead to increased political polarization, and thus, the need for automatic mitigation methods is growing. Existing mitigation work displays articles from multiple ne…
Towards Few-Shot Fact-Checking via Perplexity
Nayeon Lee, Yejin Bang, Andrea Madotto +2
Few-shot learning has drawn researchers' attention to overcome the problem of data scarcity. Recently, large pre-trained language models have shown great performance in few-shot le…
Language Models as Fact Checkers?
Nayeon Lee, Belinda Z. Li, Sinong Wang +3
Recent work has suggested that language models (LMs) store both common-sense and factual knowledge learned from pre-training data. In this paper, we leverage this implicit knowledg…
Misinformation Has High Perplexity
Nayeon Lee, Yejin Bang, Andrea Madotto +1
Debunking misinformation is an important and time-critical task as there could be adverse consequences when misinformation is not quashed promptly. However, the usual supervised ap…