1 citations · 1 across the 9 of their papers we have counts for
11 papers
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…
Agents' Last Exam
Yiyou Sun, Xinyang Han, Weichen Zhang +355
Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional d…
If Only My CGM Could Speak: A Privacy-Preserving Agent for Question Answering over Continuous Glucose Data
Yanjun Cui, Ali Emami, Temiloluwa Prioleau +1
Continuous glucose monitors (CGMs) used in diabetes care collect rich personal health data that could improve day-to-day self-management. However, current patient platforms only of…
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…
Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition
Tyler McDonald, Ali Emami
Knowledge distillation allows smaller neural networks to emulate the performance of larger, teacher models with reduced computational demands. Traditional methods for Large Languag…
Can We Afford The Perfect Prompt? Balancing Cost and Accuracy with the Economical Prompting Index
Tyler McDonald, Anthony Colosimo, Yifeng Li +1
As prompt engineering research rapidly evolves, evaluations beyond accuracy are crucial for developing cost-effective techniques. We present the Economical Prompting Index (EPI), a…
NYT-Connections: A Deceptively Simple Text Classification Task that Stumps System-1 Thinkers
Angel Yahir Loredo Lopez, Tyler McDonald, Ali Emami
Large Language Models (LLMs) have shown impressive performance on various benchmarks, yet their ability to engage in deliberate reasoning remains questionable. We present NYT-Conne…
STOP! Benchmarking Large Language Models with Sensitivity Testing on Offensive Progressions
Robert Morabito, Sangmitra Madhusudan, Tyler McDonald +1
Mitigating explicit and implicit biases in Large Language Models (LLMs) has become a critical focus in the field of natural language processing. However, many current methodologies…