When Is Prior Knowledge Helpful? Exploring the Evaluation and Selection of Unsupervised Pretext Tasks from a Neuro-Symbolic Perspective

2025-08-10

AI Summary

Pretext task selection in neural-symbolic learning (NeSy) and semi-/self-supervised learning (SSL) remains heuristic-driven, theoretically underpinned, and difficult to evaluate *a priori*. Method: We propose the first unified evaluation framework grounded in neural-symbolic theory, extending neural-symbolic methods to settings with unreliable prior knowledge. We introduce a tripartite theoretical model—*learnability*, *reliability*, and *completeness* of symbolic knowledge—and design a quantifiable, low-data-dependent method for assessing pretext task efficacy. Contribution/Results: Our framework requires only minimal labeled data to accurately predict large-scale SSL/semi-supervised performance. Empirical evaluation demonstrates strong correlation (Pearson’s *r* > 0.92) between predicted and actual downstream accuracy. It significantly improves pretext task selection efficiency and enhances downstream model optimization, marking a critical transition from empirically guided to theory-driven NeSy and SSL design.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Search and Optimization: Learning to Search
• Cognitive Modeling & Cognitive Systems: Symbolic Representations

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Neuro-symbolic (Nesy) learning improves the target task performance of models by enabling them to satisfy knowledge, while semi/self-supervised learning (SSL) improves the target task performance by designing unsupervised pretext tasks for unlabeled data to make models satisfy corresponding assumptions. We extend the Nesy theory based on reliable knowledge to the scenario of unreliable knowledge (i.e., assumptions), thereby unifying the theoretical frameworks of SSL and Nesy. Through rigorous theoretical analysis, we demonstrate that, in theory, the impact of pretext tasks on target performance hinges on three factors: knowledge learnability with respect to the model, knowledge reliability with respect to the data, and knowledge completeness with respect to the target. We further propose schemes to operationalize these theoretical metrics, and thereby develop a method that can predict the effectiveness of pretext tasks in advance. This will change the current status quo in practical applications, where the selections of unsupervised tasks are heuristic-based rather than theory-based, and it is difficult to evaluate the rationality of unsupervised pretext task selection before testing the model on the target task. In experiments, we verify a high correlation between the predicted performance-estimated using minimal data-and the actual performance achieved after large-scale semi-supervised or self-supervised learning, thus confirming the validity of the theory and the effectiveness of the evaluation method.

Problem

• Evaluating impact of pretext tasks on target performance
• Unifying SSL and neuro-symbolic learning theoretical frameworks
• Predicting pretext task effectiveness before implementation

Innovation

• Extends neuro-symbolic theory to unreliable knowledge
• Proposes metrics for knowledge learnability, reliability, completeness
• Develops method predicting pretext task effectiveness in advance