5 papers
Fine-Grained Perspectives: Modeling Explanations with Annotator-Specific Rationales
Olufunke O. Sarumi, Charles Welch, Daniel Braun
Beyond exploring disaggregated labels for modeling perspectives, annotator rationales provide fine-grained signals of individual perspectives. In this work, we propose a framework…
Acquiescence Bias in Large Language Models
Daniel Braun
Acquiescence bias, i.e. the tendency of humans to agree with statements in surveys, independent of their actual beliefs, is well researched and documented. Since Large Language Mod…
A Retail-Corpus for Aspect-Based Sentiment Analysis with Large Language Models
Oleg Silcenco, Marcos R. Machad, Wallace C. Ugulino +1
Aspect-based sentiment analysis enhances sentiment detection by associating it with specific aspects, offering deeper insights than traditional sentiment analysis. This study intro…
The Impact of Annotator Personas on LLM Behavior Across the Perspectivism Spectrum
Olufunke O. Sarumi, Charles Welch, Daniel Braun +1
In this work, we explore the capability of Large Language Models (LLMs) to annotate hate speech and abusiveness while considering predefined annotator personas within the strong-to…
Position: Editing Large Language Models Poses Serious Safety Risks
Paul Youssef, Zhixue Zhao, Daniel Braun +2
Large Language Models (LLMs) contain large amounts of facts about the world. These facts can become outdated over time, which has led to the development of knowledge editing method…