3 papers
cs.CL2025
Correct-Detect: Balancing Performance and Ambiguity Through the Lens of Coreference Resolution in LLMs
Amber Shore, Russell Scheinberg, Ameeta Agrawal +1
Large Language Models (LLMs) are intended to reflect human linguistic competencies. But humans have access to a broad and embodied context, which is key in detecting and resolving…
cs.CL2025
Who Relies More on World Knowledge and Bias for Syntactic Ambiguity Resolution: Humans or LLMs?
So Young Lee, Russell Scheinberg, Amber Shore +1
This study explores how recent large language models (LLMs) navigate relative clause attachment {ambiguity} and use world knowledge biases for disambiguation in six typologically d…
cs.CL2025
Multilingual Relative Clause Attachment Ambiguity Resolution in Large Language Models
So Young Lee, Russell Scheinberg, Amber Shore +1
This study examines how large language models (LLMs) resolve relative clause (RC) attachment ambiguities and compares their performance to human sentence processing. Focusing on tw…