Task Priors: Enhancing Model Evaluation by Considering the Entire Space of Downstream Tasks

2025-07-13

AI Summary

Current AI evaluation relies on fixed, handcrafted benchmarks, failing to reflect models’ true generalization across the space of *all possible tasks*—a critical research bottleneck. To address this, we propose the **Task Priors framework**, the first formalism that models downstream tasks as well-defined, measurable probability distributions over a theoretically grounded task space. Within this framework, we quantify model performance via two principled metrics: (i) *expected performance*—the weighted average over the task distribution—and (ii) *performance variance*—capturing generalization stability. Our approach integrates probabilistic modeling with rigorous task distribution specification, enabling systematic, unbiased cross-task evaluation—including for self-supervised and other pretraining paradigms. Extensive experiments demonstrate that the framework substantially enhances assessment comprehensiveness, scientific rigor, and scalability, establishing a foundational theoretical basis for evaluating general-purpose AI capabilities.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The grand goal of AI research, and particularly Self Supervised Learning (SSL), is to produce systems that can successfully solve any possible task. In contrast, current evaluation methods available to AI researchers typically rely on a fixed collection of hand-picked downstream benchmarks. Hence, a large amount of effort is put into designing and searching for large collection of evaluation tasks that can serve as a proxy of our grand goal. We argue that such a rigid evaluation protocol creates a silent bottleneck in AI research. To remedy that, we define a probabilistic space of downstream tasks obtained by adopting a distribution of tasks and by defining Task Priors. Under this view, one can evaluate a model's performance over the set of all possible downstream tasks. Our framework is the first to provide answers to key questions such as (i) what is the average performance of my model over all possible downstream tasks weighted by the probability to encounter each task? or (ii) what is the variance of my model's performance across all downstream tasks under the defined Task Priors? Beyond establishing a new standard for evaluation, we believe that Task Priors will accelerate the pace of research in SSL - where downstream task evaluation is the sole qualitative signal that researchers have access to.

Problem

• Evaluating AI models across all possible downstream tasks
• Addressing limitations of fixed benchmark evaluations in AI
• Introducing Task Priors to measure model performance probabilistically

Innovation

• Adopts probabilistic space of downstream tasks
• Defines Task Priors for model evaluation
• Evaluates model performance over all possible tasks