Work

TELLME: Test-Enhanced Learning for Language Model Enrichment

Year: 2026

Type: conference-paper

Abstract: Continual pre-training (CPT) has been widely adopted as a method for domain adaptation in large language models.However, CPT has consistently been accompanied by challenges, such as the difficulty of acquiring large-scale domain-specific datasets and high computational costs.In this study, we propose a novel method called Test-Enhanced Learning for Language Model Enrichment (TELLME) to alleviate these issues.TELLME leverages the Test-Enhanced Learning (TEL) principle, whereby the model's training efficiency is improved using quizzes during training.It integrates this principle with CPT, thereby promoting efficient domain-specific knowledge acquisition and long-term memory retention.Experimental results demonstrate that TELLME outperforms existing methods by up to 23.6% in the financial domain and achieves a 9.8% improvement in long-term memory retention.The model and TELLME dataset are available at huggingface.co/anonymous4459.

Source: Findings of the Association for Computational Linguistics: EACL

DOI: 10.18653/v1/2026.findings-eacl.84