2 citations · 2 across the 11 of their papers we have counts for
13 papers
Strategic Self-Consistency
Tori Qiu, Ander Artola Velasco, Manuel Gomez-Rodriguez
Self-consistency has become a popular technique for enhancing the reasoning abilities of large language models by generating multiple reasoning paths and selecting the final answer…
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…
Optimizing Social Utility in Sequential Experiments
Ander Artola Velasco, Stratis Tsirtsis, Manuel Gomez-Rodriguez
Regulatory approval of products in high-stakes domains such as drug development requires statistical evidence of safety and efficacy through large-scale randomized controlled trial…
Test-Time Compute Games
Ander Artola Velasco, Dimitrios Rontogiannis, Stratis Tsirtsis +1
Test-time compute has emerged as a promising strategy to enhance the reasoning abilities of large language models (LLMs). However, this strategy has in turn increased how much user…
Narrowing Action Choices with AI Improves Human Sequential Decisions
Eleni Straitouri, Stratis Tsirtsis, Ander Artola Velasco +1
Recent work has shown that, in classification tasks, it is possible to design decision support systems that do not require human experts to understand when to cede agency to a clas…
Auditing Pay-Per-Token in Large Language Models
Ander Artola Velasco, Stratis Tsirtsis, Manuel Gomez-Rodriguez
Millions of users rely on a market of cloud-based services to obtain access to state-of-the-art large language models. However, it has been very recently shown that the de facto pa…
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…
Is Your LLM Overcharging You? Tokenization, Transparency, and Incentives
Ander Artola Velasco, Stratis Tsirtsis, Nastaran Okati +1
State-of-the-art large language models require specialized hardware and substantial energy to operate. As a consequence, cloud-based services that provide access to large language…
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…
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…
Matchings, Predictions and Counterfactual Harm in Refugee Resettlement Processes
Seungeon Lee, Nina Corvelo Benz, Suhas Thejaswi +1
Resettlement agencies have started to adopt data-driven algorithmic matching to match refugees to locations using employment rate as a measure of utility. Given a pool of refugees,…
Towards Human-AI Complementarity with Prediction Sets
Giovanni De Toni, Nastaran Okati, Suhas Thejaswi +2
Decision support systems based on prediction sets have proven to be effective at helping human experts solve classification tasks. Rather than providing single-label predictions, t…