5 papers
SHAP-based Explanations are Sensitive to Feature Representation
Hyunseung Hwang, Andrew Bell, Joao Fonseca +3
Local feature-based explanations are a key component of the XAI toolkit. These explanations compute feature importance values relative to an ``interpretable'' feature representatio…
Faster, Cheaper, Better: Multi-Objective Hyperparameter Optimization for LLM and RAG Systems
Matthew Barker, Andrew Bell, Evan Thomas +3
While Retrieval Augmented Generation (RAG) has emerged as a popular technique for improving Large Language Model (LLM) systems, it introduces a large number of choices, parameters…
Output Scouting: Auditing Large Language Models for Catastrophic Responses
Andrew Bell, Joao Fonseca
Recent high profile incidents in which the use of Large Language Models (LLMs) resulted in significant harm to individuals have brought about a growing interest in AI safety. One r…
Safeguarding Large Language Models in Real-time with Tunable Safety-Performance Trade-offs
Joao Fonseca, Andrew Bell, Julia Stoyanovich
Large Language Models (LLMs) have been shown to be susceptible to jailbreak attacks, or adversarial attacks used to illicit high risk behavior from a model. Jailbreaks have been ex…
Making Transparency Advocates: An Educational Approach Towards Better Algorithmic Transparency in Practice
Andrew Bell, Julia Stoyanovich
Concerns about the risks and harms posed by artificial intelligence (AI) have resulted in significant study into algorithmic transparency, giving rise to a sub-field known as Expla…