1 citations · 1 across the 5 of their papers we have counts for
5 papers · 1 filter
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…
Explain-then-Process: Using Grammar Prompting to Enhance Grammatical Acceptability Judgments
Russell Scheinberg, Ameeta Agrawal, Amber Shore +1
Large language models (LLMs) can explain grammatical rules, yet they often fail to apply those rules when judging sentence acceptability. We present "grammar prompting", an explain…
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…
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…
Evaluating Multilingual Long-Context Models for Retrieval and Reasoning
Ameeta Agrawal, Andy Dang, Sina Bagheri Nezhad +2
Recent large language models (LLMs) demonstrate impressive capabilities in handling long contexts, some exhibiting near-perfect recall on synthetic retrieval tasks. However, these…