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Addison J. Wu

3 accepted papers

2026

Large Language Models Develop Novel Social Biases Through Adaptive Exploration

ICML 2026oral

As large language models (LLMs) are adopted into frameworks that grant them the capacity to make real decisions, it is increasingly important to ensure that they are unbiased. In this paper, we argue that the predominant approach of simply removing existing biases from models is not enough. Using a …

Cited by 0SourceScholar
2025

Are Large Language Models Sensitive to the Motives Behind Communication?

NeurIPS 2025poster

Human communication is $\textit{motivated}$: people speak, write, and create content with a particular communicative intent in mind. As a result, information that large language models (LLMs) and AI agents process is inherently framed by humans' intentions and incentives. People are adept at navigat…

Cited by 0SourceScholar
2025

Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse

ICML 2025poster

Chain-of-thought (CoT) prompting has become a widely used strategy for improving large language and multimodal model performance. However, it is still an open question under which settings CoT systematically reduces performance. In this paper, we seek to identify the characteristics of tasks where…

Cited by 21SourcePDFScholar