4 citations · 7 across the 10 of their papers we have counts for
6 papers · 1 filter
Cognitive Expert Language Models Better Align with the Corresponding Brain Systems
Zhivar Sourati, Mengxuan Helen Wu, Nona Ghazizadeh +3
Large language models (LLMs) can predict human brain activity across a variety of brain regions during natural language comprehension. Typically, however, LLM-brain alignment is me…
Structural Abstraction as an Inductive Bias for Non-Stationary Language Model Training
Elnaz Rahmati, Nona Ghazizadeh, Zhivar Sourati +2
A foundational principle in cognitive science holds that intelligent agents do not learn by storing experiences as isolated instances, but by forming abstract schemas that capture…
Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment
Jialu Wang, Heinrich Peters, Asad A. Butt +6
Despite their sophisticated general-purpose capabilities, Large Language Models (LLMs) often fail to align with diverse individual preferences because standard post-training method…
The Subjectivity of Respect in Police Traffic Stops: Modeling Community Perspectives in Body-Worn Camera Footage
Preni Golazizian, Elnaz Rahmati, Jackson Trager +17
Traffic stops are among the most frequent police-civilian interactions, and body-worn cameras (BWCs) provide a unique record of how these encounters unfold. Respect is a central di…
Theory Trace Card: Theory-Driven Socio-Cognitive Evaluation of LLMs
Farzan Karimi-Malekabadi, Suhaib Abdurahman, Zhivar Sourati +2
Socio-cognitive benchmarks for large language models (LLMs) often fail to predict real-world behavior, even when models achieve high benchmark scores. Prior work has attributed thi…
Tracing Moral Foundations in Large Language Models
Chenxiao Yu, Bowen Yi, Farzan Karimi-Malekabadi +5
Large language models often produce human-like moral judgments, but it is unclear whether this reflects an internal conceptual structure or superficial ``moral mimicry.'' Using Mor…