7 citations · 12 across the 12 of their papers we have counts for
12 papers
Europe and the Geopolitics of AGI: The Need for a Preparedness Plan
Maximilian Negele, Daan Juijn, Afek Shamir +8
Artificial general intelligence (AGI)--defined here as AI systems that match or exceed humans at most economically useful cognitive work--has moved from speculation to the centre o…
Seven simple steps for log analysis in AI systems
Magda Dubois, Ekin Zorer, Maia Hamin +17
AI systems produce large volumes of logs as they interact with tools and users. Analysing these logs can help understand model capabilities, propensities, and behaviours, or assess…
Capabilities Ain't All You Need: Measuring Propensities in AI
Daniel Romero-Alvarado, Fernando Martínez-Plumed, Lorenzo Pacchiardi +11
AI evaluation has primarily focused on measuring capabilities, with formal approaches inspired from Item Response Theory (IRT) being increasingly applied. Yet propensities - the te…
Frontier AI Auditing: Toward Rigorous Third-Party Assessment of Safety and Security Practices at Leading AI Companies
Miles Brundage, Noemi Dreksler, Aidan Homewood +45
We outline a vision for frontier AI auditing, which we define as rigorous third-party verification of frontier AI developers' safety and security claims, and evaluation of their sy…
No Answer Needed: Predicting LLM Answer Accuracy from Question-Only Linear Probes
Iván Vicente Moreno Cencerrado, Arnau Padrés Masdemont, Anton Gonzalvez Hawthorne +2
Do large language models (LLMs) anticipate when they will answer correctly? To study this, we extract activations after a question is read but before any tokens are generated, and…
Measuring Data Science Automation: A Survey of Evaluation Tools for AI Assistants and Agents
Irene Testini, José Hernández-Orallo, Lorenzo Pacchiardi
Data science aims to extract insights from data to support decision-making processes. Recently, Large Language Models (LLMs) have been increasingly used as assistants for data scie…
General Scales Unlock AI Evaluation with Explanatory and Predictive Power
Lexin Zhou, Lorenzo Pacchiardi, Fernando Martínez-Plumed +23
Ensuring safe and effective use of AI requires understanding and anticipating its performance on novel tasks, from advanced scientific challenges to transformed workplace activitie…
Paradigms of AI Evaluation: Mapping Goals, Methodologies and Culture
John Burden, Marko Tešić, Lorenzo Pacchiardi +1
Research in AI evaluation has grown increasingly complex and multidisciplinary, attracting researchers with diverse backgrounds and objectives. As a result, divergent evaluation pa…
PredictaBoard: Benchmarking LLM Score Predictability
Lorenzo Pacchiardi, Konstantinos Voudouris, Ben Slater +4
Despite possessing impressive skills, Large Language Models (LLMs) often fail unpredictably, demonstrating inconsistent success in even basic common sense reasoning tasks. This unp…
Leaving the barn door open for Clever Hans: Simple features predict LLM benchmark answers
Lorenzo Pacchiardi, Marko Tesic, Lucy G. Cheke +1
The integrity of AI benchmarks is fundamental to accurately assess the capabilities of AI systems. The internal validity of these benchmarks - i.e., making sure they are free from…
100 instances is all you need: predicting the success of a new LLM on unseen data by testing on a few instances
Lorenzo Pacchiardi, Lucy G. Cheke, José Hernández-Orallo
Predicting the performance of LLMs on individual task instances is essential to ensure their reliability in high-stakes applications. To do so, a possibility is to evaluate the con…
How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions
Lorenzo Pacchiardi, Alex J. Chan, Sören Mindermann +5
Large language models (LLMs) can "lie", which we define as outputting false statements despite "knowing" the truth in a demonstrable sense. LLMs might "lie", for example, when inst…