TELLME: Test-Enhanced Learning Boosts Language Model Domain Adaptation

A new approach called TELLME (Test-Enhanced Learning for Language Model Enrichment) has been developed by researchers, integrating principles of test-enhanced learning with ongoing pre-training to enhance domain adaptation in large language models. This method tackles prevalent issues in continual pre-training, including the challenges of obtaining extensive domain-specific datasets and the associated high computational expenses. By incorporating quizzes during the training process, TELLME boosts efficiency and aids in long-term memory retention. Experimental findings indicate that TELLME surpasses current methods by as much as 23.6% in the financial sector and shows a 9.8% increase in long-term memory retention. The research paper can be found on arXiv with the identifier 2608.11788.