2 citations · 2 across the 11 of their papers we have counts for
12 papers
The Sample Complexity of Quantum Entanglement Allocation
Nathan Roll
How many past requests are needed to decide which qubits should share entanglement? We show that the answer depends on the allocation choices created by the queries: a larger memor…
Orukeet: Multilingual ASR with Frozen Gabor Kernels
Nathan Roll, Irene Yi, Büşra Marşan +6
Orukeet replaces half of an adapted Parakeet encoder's temporal filters with 12,288 fitted Gabor kernels, freezes these replacements, and trains the remaining parameters on multili…
Clinical Reasoning in the Age of AI: Longitudinal Cognition and Human-AI Collaboration
Irene Yi, Grace Brown, Sufian Aldogom +10
As physicians turn to AI-powered systems to help meet the dual demands of speed and care quality, they are met with hallucinations and sycophancy. Understanding how doctors reason…
Beyond Prediction: Longitudinal Reasoning in EHR-Integrated Clinical AI
Irene Yi, Grace Brown, Sufian Aldogom +8
We present a structured analysis of how contemporary clinical AI systems integrate electronic health record (EHR) data and the extent to which they support longitudinal clinical re…
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…
Entanglement as Memory: Mechanistic Interpretability of Quantum Language Models
Nathan Roll
Quantum language models have shown competitive performance on sequential tasks, yet whether trained quantum circuits exploit genuinely quantum resources -- or merely embed classica…
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…
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…
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…
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…
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…
PSST! Prosodic Speech Segmentation with Transformers
Nathan Roll, Calbert Graham, Simon Todd
Self-attention mechanisms have enabled transformers to achieve superhuman-level performance on many speech-to-text (STT) tasks, yet the challenge of automatic prosodic segmentation…