6 citations · 8 across the 7 of their papers we have counts for
5 papers · 1 filter
PROPEX-RAG: Enhanced GraphRAG using Prompt-Driven Prompt Execution
Tejas Sarnaik, Manan Shah, Ravi Hegde
Retrieval-Augmented Generation (RAG) has become a robust framework for enhancing Large Language Models (LLMs) with external knowledge. Recent advances in RAG have investigated grap…
"Amazing, They All Lean Left" -- Analyzing the Political Temperaments of Current LLMs
W. Russell Neuman, Chad Coleman, Ali Dasdan +3
Recent studies have revealed a consistent liberal orientation in the ethical and political responses generated by most commercial large language models (LLMs), yet the underlying c…
The Convergent Ethics of AI? Analyzing Moral Foundation Priorities in Large Language Models with a Multi-Framework Approach
Chad Coleman, W. Russell Neuman, Ali Dasdan +2
As large language models (LLMs) are increasingly deployed in consequential decision-making contexts, systematically assessing their ethical reasoning capabilities becomes a critica…
Auditing the Ethical Logic of Generative AI Models
W. Russell Neuman, Chad Coleman, Ali Dasdan +2
As generative AI models become increasingly integrated into high-stakes domains, the need for robust methods to evaluate their ethical reasoning becomes increasingly important. Thi…
Analyzing the Ethical Logic of Eight Large Language Models
W. Russell Neuman, Chad Coleman, Manan Shah
This study examines the expressed ethical logic of eight prominent large language models from OpenAI, Meta, Perplexity, Anthropic, Google, Mistral, DeepSeek, and xAI. Each model an…