Aligning the Pretraining and Finetuning Objectives of Language Models (2002.02000v1)

Abstract: We demonstrate that explicitly aligning the pretraining objectives to the finetuning objectives in LLM training significantly improves the finetuning task performance and reduces the minimum amount of finetuning examples required. The performance margin gained from objective alignment allows us to build LLMs with smaller sizes for tasks with less available training data. We provide empirical evidence of these claims by applying objective alignment to concept-of-interest tagging and acronym detection tasks. We found that, with objective alignment, our 768 by 3 and 512 by 3 transformer LLMs can reach accuracy of 83.9%/82.5% for concept-of-interest tagging and 73.8%/70.2% for acronym detection using only 200 finetuning examples per task, outperforming the 768 by 3 model pretrained without objective alignment by +4.8%/+3.4% and +9.9%/+6.3%. We name finetuning small LLMs in the presence of hundreds of training examples or less "Few Example learning". In practice, Few Example Learning enabled by objective alignment not only saves human labeling costs, but also makes it possible to leverage LLMs in more real-time applications.