5 papers
Can we trust LLM Self-Explanations for Entity Resolution?
Tommaso Teofili, Donatella Firmani, Nick Koudas +2
Large Language Models (LLMs) have recently shown strong performance on Entity Resolution (ER). Additionally, akin to their prowess in providing accurate predictions, these models o…
The Open Source Economic Index of AI Adoption and Capability
Seamus Somerstep, Aritra Guha, Divesh Srivastava +1
We work towards measuring both AI adoption and the capability of AI to perform discrete labor tasks across various occupations. To measure adoption, we develop an open-source econo…
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…
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…