Open-Ethical AI: Advancements in Open-Source Human-Centric Neural Language Models

Abstract

This survey summarizes the most recent methods for building and assessing helpful, honest, and harmless neural language models, considering small, medium, and large-size models. Pointers to open-source resources that help to align pre-trained models are given, including methods that use parameter-efficient techniques, specialized prompting frameworks, adapter modules, case-specific knowledge injection, and adversarially robust training techniques. Special care is given to evidencing recent progress on value alignment, commonsense reasoning, factuality enhancement, and abstract reasoning of language models. Most reviewed works in this survey publicly shared their code and related data and were accepted in world-leading Machine Learning venues. This work aims to help researchers and practitioners accelerate their entrance into the field of human-centric neural language models, which might be a cornerstone of the contemporary and near-future industrial and societal revolution.