4 citations · 10 across the 15 of their papers we have counts for
6 papers · 2 filters
Improving and Assessing the Fidelity of Large Language Models Alignment to Online Communities
Minh Duc Chu, Zihao He, Rebecca Dorn +1
Large language models (LLMs) have shown promise in representing individuals and communities, offering new ways to study complex social dynamics. However, effectively aligning LLMs…
COMMUNITY-CROSS-INSTRUCT: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities
Zihao He, Minh Duc Chu, Rebecca Dorn +2
Social scientists use surveys to probe the opinions and beliefs of populations, but these methods are slow, costly, and prone to biases. Recent advances in large language models (L…
Don't Blame the Data, Blame the Model: Understanding Noise and Bias When Learning from Subjective Annotations
Abhishek Anand, Negar Mokhberian, Prathyusha Naresh Kumar +5
Researchers have raised awareness about the harms of aggregating labels especially in subjective tasks that naturally contain disagreements among human annotators. In this work we…
Reading Between the Tweets: Deciphering Ideological Stances of Interconnected Mixed-Ideology Communities
Zihao He, Ashwin Rao, Siyi Guo +2
Recent advances in NLP have improved our ability to understand the nuanced worldviews of online communities. Existing research focused on probing ideological stances treats liberal…
How Susceptible are Large Language Models to Ideological Manipulation?
Kai Chen, Zihao He, Jun Yan +2
Large Language Models (LLMs) possess the potential to exert substantial influence on public perceptions and interactions with information. This raises concerns about the societal i…
Whose Emotions and Moral Sentiments Do Language Models Reflect?
Zihao He, Siyi Guo, Ashwin Rao +1
Language models (LMs) are known to represent the perspectives of some social groups better than others, which may impact their performance, especially on subjective tasks such as c…