1 citations · 1 across the 9 of their papers we have counts for
5 papers · 2 filters
Comparison of techniques for fine-tuning open-weight models for entity extraction from radiology reports
Aawez Mansuri, Kush Mehta, Mohammadreza Chavoshi +10
Converting free-text radiology reports into structured labels supports cohort building, quality assurance, and monitoring of clinical imaging models, but the strongest label extrac…
Quizzing the Translation: A Prover-Grounded Evaluation Metric for NLFOL
Pu Suo, Ali Emami
A standard pipeline for symbolic reasoning over natural-language problems translates them into first-order logic and invokes a theorem prover. The translation step is the bottlenec…
What Models Express, Suppress, and Resist: Auditing Open-Weight LLMs with Persona Vectors
Winston Zeng, Ali Emami, Jinho D. Choi
What a language model will and will not do is largely set during post-training, but which behaviors it expresses, hides, or resists is not revealed by prompting alone. Persona vect…
DART: Mitigating Harm Drift in Difference-Aware LLMs via Distill-Audit-Repair Training
Ziwen Pan, Zihan Liang, Jad Kabbara +1
Large language models (LLMs) tuned for safety often avoid acknowledging demographic differences, even when such acknowledgment is factually correct (e.g., ancestry-based disease in…
Memory Dial: A Training Framework for Controllable Memorization in Language Models
Xiangbo Zhang, Ali Emami
Memorization in language models is widely studied but remains difficult to isolate and control. Understanding when and what models memorize is essential for explaining their predic…