Economizing Large Language Model Training and Alignment with Human Values through CostEffective Architectures and Transfer Learning Techniques

This work uses transfer learning and cost-effective methods to train large language models (LLMs) with human values. Lack of ethical consistency and expensive computational costs have hindered LLM advancement. The research seeks to solve all issues. Even if they improve AI, learning LLMs may bring moral problems and processing costs. These beliefs must respect human rights and values to be ethical. The project seeks innovative approaches to reduce training expenses without compromising LLM quality. The proposed strategy relies on low-cost, shared-learning building designs. Transfer learning is applying the models you’ve learned to new situations without additional instruction. We also evaluate many lightweight model designs. A further precaution to guarantee that people’s values are preserved is to subject LLMs to an ethical review procedure. The models are more efficient than comparable models, although they need fewer computational resources.