Domain Obedient Deep Learning

Abstract

Deep learning, a family of data-driven artificial intelligence techniques, has shown immense promise in a plethora of applications, and it has even outpaced experts in several domains. However, unlike symbolic approaches to learning, these methods fall short when it comes to abiding by and learning from pre-existing established principles. This is a significant deficit for deployment in critical applications such as robotics, medicine, industrial automation, etc. For a decision system to be considered for adoption in such fields, it must demonstrate the ability to adhere to specified constraints, an ability missing in deep learning-based approaches. Exploring this problem serves as the core tenet of this dissertation.

This dissertation starts with an exploration of the abilities of conventional deep learning-based systems vis-à-vis domain coherence. A large-scale rule-annotated dataset is introduced to mitigate the pronounced lack of suitable constraint adherence evaluation benchmarks, and with its aid, the rule adherence abilities of vision systems are analyzed. Additionally, this study probes language models to elicit their performance characteristics with regard to domain consistency. Examination of these language models with interventions illustrates their ineptitude at obeying domain principles, and a mitigation strategy is proposed. This is followed by an exploration of techniques for imbuing deep learning systems with domain constraint information. Also, a comprehensive study of standard evaluation metrics and their blind spots pertaining to domain-aware performance estimation is undertaken. Finally, a novel technique to enforce constraint compliance in models without training is introduced, which pairs a search strategy with large language models to achieve cutting-edge performance.

A key highlight of this dissertation is the emphasis on addressing pertinent real-world problems with scalable and practicable solutions. We hope the results presented here pave the way for wider adoption of deep learning-based systems in pivotal situations with enhanced confidence in their trustworthiness.