most citedEvaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs

1 citations · 2 across the 5 of their papers we have counts for

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5 papers

cs.AI2025

Kantian-Utilitarian XAI: Meta-Explained

Zahra Atf, Peter R. Lewis

We present a gamified explainable AI (XAI) system for ethically aware consumer decision-making in the coffee domain. Each session comprises six rounds with three options per round.…

cs.CL2025

ScenarioBench: Trace-Grounded Compliance Evaluation for Text-to-SQL and RAG

Zahra Atf, Peter R Lewis

ScenarioBench is a policy-grounded, trace-aware benchmark for evaluating Text-to-SQL and retrieval-augmented generation in compliance contexts. Each YAML scenario includes a no-pee…

cs.CL2025

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation

Zahra Atf, Peter R Lewis

Large language models (LLMs) are increasingly used in high-stakes settings, where explaining uncertainty is both technical and ethical. Probabilistic methods are often opaque and m…

cs.CY20251 cited

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs

Nariman Naderi, Zahra Atf, Peter R Lewis +3

This paper investigates how prompt engineering techniques impact both accuracy and confidence elicitation in Large Language Models (LLMs) applied to medical contexts. Using a strat…

cs.AI20251 cited

The challenge of uncertainty quantification of large language models in medicine

Zahra Atf, Seyed Amir Ahmad Safavi-Naini, Peter R. Lewis +4

This study investigates uncertainty quantification in large language models (LLMs) for medical applications, emphasizing both technical innovations and philosophical implications.…