7 papers
Implicit vs. Explicit Prompting Strategies for LVLMs in Referential Communication
Peter Zeng, Amie J. Paige, Weiling Li +3
Two recent studies (Jones et al. (2026); Zeng et al. (2026)) reach apparently contradictory conclusions about whether LVLMs can coordinate on efficient referring expressions. We co…
Gram2Vec: An Interpretable Document Vectorizer
Peter Zeng, Hannah Stortz, Eric Sclafani +4
We present Gram2Vec, a grammatical style embedding system that embeds documents into a higher dimensional space by extracting the normalized relative frequencies of grammatical fea…
Residualized Similarity for Faithfully Explainable Authorship Verification
Peter Zeng, Pegah Alipoormolabashi, Jihu Mun +5
Responsible use of Authorship Verification (AV) systems not only requires high accuracy but also interpretable solutions. More importantly, for systems to be used to make decisions…
OmniVox: Zero-Shot Emotion Recognition with Omni-LLMs
John Murzaku, Owen Rambow
The use of omni-LLMs (large language models that accept any modality as input), particularly for multimodal cognitive state tasks involving speech, is understudied. We present Omni…
Active Few-Shot Learning for Text Classification
Saeed Ahmadnia, Arash Yousefi Jordehi, Mahsa Hosseini Khasheh Heyran +3
The rise of Large Language Models (LLMs) has boosted the use of Few-Shot Learning (FSL) methods in natural language processing, achieving acceptable performance even when working w…
Zero-Shot Belief: A Hard Problem for LLMs
John Murzaku, Owen Rambow
We present two LLM-based approaches to zero-shot source-and-target belief prediction on FactBank: a unified system that identifies events, sources, and belief labels in a single pa…