6 papers · 1 filter
Categorize Early, Integrate Late: Divergent Processing Strategies in Automatic Speech Recognition
Nathan Roll, Pranav Bhalerao, Martijn Bartelds +7
In speech language modeling, two architectures dominate the frontier: the Transformer and the Conformer. However, it remains unknown whether their comparable performance stems from…
Artificial Aphasias in Lesioned Language Models
Nathan Roll, Jill Kries, Laura Gwilliams +1
Aphasias, selective language impairments which can arise from brain damage, reveal the functional organization of human language by providing causal links between affected brain re…
The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices?
Chen Shani, Yuval Reif, Nathan Roll +2
Multilingual language models (LMs) promise broader NLP access, yet current systems deliver uneven performance across the world's languages. This survey examines why these gaps pers…
The Text Aphasia Battery (TAB): A Clinically-Grounded Benchmark for Aphasia-Like Deficits in Language Models
Nathan Roll, Jill Kries, Flora Jin +5
Large language models (LLMs) have emerged as a candidate "model organism" for human language, offering an unprecedented opportunity to study the computational basis of linguistic d…
PolyPrompt: Automating Knowledge Extraction from Multilingual Language Models with Dynamic Prompt Generation
Nathan Roll
Large language models (LLMs) showcase increasingly impressive English benchmark scores, however their performance profiles remain inconsistent across multilingual settings. To addr…
In-Context Learning Boosts Speech Recognition via Human-like Adaptation to Speakers and Language Varieties
Nathan Roll, Calbert Graham, Yuka Tatsumi +3
Human listeners readily adjust to unfamiliar speakers and language varieties through exposure, but do these adaptation benefits extend to state-of-the-art spoken language models? W…