42 citations · 54 across the 12 of their papers we have counts for
13 papers
Position: Evaluations of AI Moral Reasoning Still Miss Half of the Picture
Aidan Kierans, Ritam Dutt, Kaley Rittichier +2
Recent work on evaluating the moral competence of large language models (LLMs) has focused primarily on what we call the moral value problem, i.e., whether model outputs align with…
AI-Powered Detection of Inappropriate Language in Medical School Curricula
Chiman Salavati, Shannon Song, Scott A. Hale +3
The use of inappropriate language -- such as outdated, exclusionary, or non-patient-centered terms -- medical instructional materials can significantly influence clinical training,…
A Scaling Law for Token Efficiency in LLM Fine-Tuning Under Fixed Compute Budgets
Ryan Lagasse, Aidan Kierans, Avijit Ghosh +1
We introduce a scaling law for fine-tuning large language models (LLMs) under fixed compute budgets that explicitly accounts for data composition. Conventional approaches measure t…
Stop treating `AGI' as the north-star goal of AI research
Borhane Blili-Hamelin, Christopher Graziul, Leif Hancox-Li +13
The AI research community plays a vital role in shaping the scientific, engineering, and societal goals of AI research. In this position paper, we argue that focusing on the highly…
Towards Fairer Health Recommendations: finding informative unbiased samples via Word Sense Disambiguation
Gavin Butts, Pegah Emdad, Jethro Lee +5
There have been growing concerns around high-stake applications that rely on models trained with biased data, which consequently produce biased predictions, often harming the most…
ClaimCompare: A Data Pipeline for Evaluation of Novelty Destroying Patent Pairs
Arav Parikh, Shiri Dori-Hacohen
A fundamental step in the patent application process is the determination of whether there exist prior patents that are novelty destroying. This step is routinely performed by both…