3 citations · 4 across the 9 of their papers we have counts for
9 papers
RPAM: A Principled Metric for Evaluating Associations in Language Models with High Predictive Validity in Downstream Outputs
Damian Hodel, Jevin West, Aylin Caliskan
Language models (LMs) exhibit problematic biases, such as stereotypes. Effectively analyzing and mitigating such biases requires accurate and generalizable evaluation methods of th…
Training LLMs with Reinforcement Learning for Intent-Aware Personalized Question Answering
Maryam Amirizaniani, Benjamin Charles Germain Lee, Jevin West +1
Effective personalized question answering (PQA) in language models requires grounding responses in the user's underlying intent, where intent refers to the implicit ``why'' behind…
The Cost of Consensus: Malignant Epistemic Herding and Adaptive Gating in Distributed Multi-Agent Search
David Farr, Iain Cruickshank, Kate Starbird +1
Distributed agents in real-world settings frequently must coordinate under uncertainty with only partial observations. Coordination is necessary to share beliefs to aid in task com…
Temporal Narrative Monitoring in Dynamic Information Environments
David Farr, Stephen Prochaska, Jack Moody +4
Comprehending the information environment (IE) during crisis events is challenging due to the rapid change and abstract nature of the domain. Many approaches focus on snapshots via…
Simulating Misinformation Vulnerabilities With Agent Personas
David Farr, Lynnette Hui Xian Ng, Stephen Prochaska +2
Disinformation campaigns can distort public perception and destabilize institutions. Understanding how different populations respond to information is crucial for designing effecti…
Expert-in-the-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection
David Farr, Kevin Talty, Alexandra Farr +3
As cyber threats become more sophisticated, rapid and accurate vulnerability detection is essential for maintaining secure systems. This study explores the use of Large Language Mo…
LLM Confidence Evaluation Measures in Zero-Shot CSS Classification
David Farr, Iain Cruickshank, Nico Manzonelli +3
Assessing classification confidence is critical for leveraging large language models (LLMs) in automated labeling tasks, especially in the sensitive domains presented by Computatio…
LLM Chain Ensembles for Scalable and Accurate Data Annotation
David Farr, Nico Manzonelli, Iain Cruickshank +2
The ability of large language models (LLMs) to perform zero-shot classification makes them viable solutions for data annotation in rapidly evolving domains where quality labeled da…
RED-CT: A Systems Design Methodology for Using LLM-labeled Data to Train and Deploy Edge Classifiers for Computational Social Science
David Farr, Nico Manzonelli, Iain Cruickshank +1
Large language models (LLMs) have enhanced our ability to rapidly analyze and classify unstructured natural language data. However, concerns regarding cost, network limitations, an…