ADeLe: Predicting and explaining AI performance across tasks

At a glance - AI benchmarks report performance on specific tasks but provide limited insight into underlying capabilities; ADeLe evaluates models by scoring both tasks and models across 18 core abilities, enabling direct comparison between task demands and model capabilities. - Using these ability scores, the method predicts performance on new tasks with ~88% accuracy, including for models such as GPT-4o and Llama-3.1. - It builds ability profiles and identifies where models are likely to succeed or fail, highlighting strengths and limitations across tasks. - By linking outcomes to task demands, ADeLe explains differences in performance, showing how it changes as task complexity increases. AI benchmarks report how large language models (LLMs) perform on specific tasks but provide little insight into their underlying capabilities that drive their performance. They do not explain failures or reliably predict outcomes on new tasks. To address this, Microsoft researchers in collaboration with Princeton University and Universitat Politècnica de València introduce ADeLe (opens in new tab) (AI Evaluation with Demand Levels), a method that characterizes both models and tasks using a broad set of capabilities, such as reasoning and domain knowledge, so performance on new tasks can be predicted and linked to specific strengths and weaknesses in a model. In a paper published in Nature, “General Scales Unlock AI Evaluation with Explanatory and Predictive Power (opens in new tab),” the team describes how ADeLe moves beyond aggregate benchmark scores. Rather than treating evaluation as a collection of isolated tests, it represents both benchmarks and LLMs using the same set of capability scores. These scores can then be used to estimate how a model will perform on tasks it has not encountered before. The research was supported by Microsoft’s Accelerating Foundation Models Research (AFMR) grant program. ADeLe-based evaluation ADeLe scores tasks across 18 core abilities, such as attention, reasoning, domain knowledge, and assigns each task a value from 0 to 5 based on how much it requires each ability. For example, a basic arithmetic problem might score low on quantitative reasoning, but an Olympiad-level proof would score much higher. Evaluating a model across many such tasks produces an ability profile—a structured view of where the model performs and where it breaks down. Comparing this profile to the demands of a new task makes it possible to identify the specific gaps that lead to failure. The process is illustrated in Figure 1. Evaluating ADeLe Using ADeLe, the team evaluated a range of AI benchmarks and model behaviors to understand what current evaluations capture and what they miss. The results show that many widely used benchmarks provide an incomplete and sometimes misleading picture of model capabilities and that a more structured approach can clarify those gaps and help predict how models will behave in new settings. ADeLe shows that many benchmarks do not isolate the abilities they are intended to measure or only cover a limited range of difficulty levels. For example, a test designed to evaluate logical reasoning may also depend heavily on specialized knowledge or metacognition.…