4 papers
RUBEN: Rule-Based Explanations for Retrieval-Augmented LLM Systems
Joel Rorseth, Parke Godfrey, Lukasz Golab +2
This paper demonstrates RUBEN, an interactive tool for discovering minimal rules to explain the outputs of retrieval-augmented large language models (LLMs) in data-driven applicati…
Rule-Based Explanations for Retrieval-Augmented LLM Systems
Joel Rorseth, Parke Godfrey, Lukasz Golab +2
If-then rules are widely used to explain machine learning models; e.g., "if employed = no, then loan application = rejected." We present the first proposal to apply rules to explai…
Explaining Expert Search and Team Formation Systems with ExES
Kiarash Golzadeh, Lukasz Golab, Jaroslaw Szlichta
Expert search and team formation systems operate on collaboration networks, with nodes representing individuals, labeled with their skills, and edges denoting collaboration relatio…
RAGE Against the Machine: Retrieval-Augmented LLM Explanations
Joel Rorseth, Parke Godfrey, Lukasz Golab +2
This paper demonstrates RAGE, an interactive tool for explaining Large Language Models (LLMs) augmented with retrieval capabilities; i.e., able to query external sources and pull r…