7 papers
Learning to Decide with AI Assistance under Human-Alignment
Nina Corvelo Benz, Eleni Straitouri, Manuel Gomez-Rodriguez
It is widely agreed that when AI models assist decision-makers in high-stakes domains by predicting an outcome of interest, they should communicate the confidence of their predicti…
Towards Human-AI Complementarity in Matching Tasks
Adrian Arnaiz-Rodriguez, Nina Corvelo Benz, Suhas Thejaswi +2
Data-driven algorithmic matching systems promise to help human decision makers make better matching decisions in a wide variety of high-stakes application domains, such as healthca…
Tokenization Multiplicity Leads to Arbitrary Price Variation in LLM-as-a-service
Ivi Chatzi, Nina Corvelo Benz, Stratis Tsirtsis +1
Providers of LLM-as-a-service have predominantly adopted a simple pricing model: users pay a fixed price per token. Consequently, one may think that the price two different users w…
Evaluation of Large Language Models via Coupled Token Generation
Nina Corvelo Benz, Stratis Tsirtsis, Eleni Straitouri +4
State of the art large language models rely on randomization to respond to a prompt. As an immediate consequence, a model may respond differently to the same prompt if asked multip…
Human-Alignment Influences the Utility of AI-assisted Decision Making
Nina L. Corvelo Benz, Manuel Gomez Rodriguez
Whenever an AI model is used to predict a relevant (binary) outcome in AI-assisted decision making, it is widely agreed that, together with each prediction, the model should provid…
Counterfactual Token Generation in Large Language Models
Ivi Chatzi, Nina Corvelo Benz, Eleni Straitouri +2
"Sure, I am happy to generate a story for you: Captain Lyra stood at the helm of her trusty ship, the Maelstrom's Fury, gazing out at the endless sea. [...] Lyra's eyes welled up w…