1 citations · 3 across the 6 of their papers we have counts for
6 papers
Smoothing Out Hallucinations: Mitigating LLM Hallucination with Smoothed Knowledge Distillation
Hieu Nguyen, Zihao He, Shoumik Atul Gandre +3
Large language models (LLMs) often suffer from hallucination, generating factually incorrect or ungrounded content, which limits their reliability in high-stakes applications. A ke…
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…
The Pulse of Mood Online: Unveiling Emotional Reactions in a Dynamic Social Media Landscape
Siyi Guo, Zihao He, Ashwin Rao +3
The rich and dynamic information environment of social media provides researchers, policy makers, and entrepreneurs with opportunities to learn about social phenomena in a timely m…
Inducing Political Bias Allows Language Models Anticipate Partisan Reactions to Controversies
Zihao He, Siyi Guo, Ashwin Rao +1
Social media platforms are rife with politically charged discussions. Therefore, accurately deciphering and predicting partisan biases using Large Language Models (LLMs) is increas…
Socio-Linguistic Characteristics of Coordinated Inauthentic Accounts
Keith Burghardt, Ashwin Rao, Siyi Guo +6
Online manipulation is a pressing concern for democracies, but the actions and strategies of coordinated inauthentic accounts, which have been used to interfere in elections, are n…