5 papers
Where did the ambiguity go? Examining how multimodal models interpret polysemous words
Jasin Cekinmez, Addison J. Wu, Raja Marjieh +1
Human language is highly polysemous. Many common words (e.g., "bank" or "palm") carry several distinct meanings that shape what humans communicate and imagine. Large language model…
SportD: How do VLMs physically strategize?
Jasin Cekinmez, Addison J. Wu, Haotian Xia +6
Vision-language models (VLMs) can describe a scene, but can they act well within one? We study whether VLMs can make sound strategic decisions, using soccer as an objective testbed…
Query Timing Produces Opposite Positional Biases Between LLMs and Humans
Jasin Cekinmez, Addison J. Wu, Thomas L. Griffiths
Positional biases such as recency and primacy effects have been documented in large language models (LLMs), yet the underlying mechanism by which these models make their evaluation…
Are Large Language Models Sensitive to the Motives Behind Communication?
Addison J. Wu, Ryan Liu, Kerem Oktar +2
Human communication is motivated: people speak, write, and create content with a particular communicative intent in mind. As a result, information that large language models (LLMs)…
Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse
Ryan Liu, Jiayi Geng, Addison J. Wu +3
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…