TELLME: Test-Enhanced Learning for Language Model Enrichment
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Computer Science > Computation and Language
Title:TELLME: Test-Enhanced Learning for Language Model Enrichment
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 TestEnhanced 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.
| Comments: | Findings of the Association for Computational Linguistics: EACL 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.11788 [cs.CL] |
| (or arXiv:2608.11788v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.11788
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Findings of the Association for Computational Linguistics: EACL 2026, pages 1655-1677 |
| Related DOI: | https://doi.org/10.18653/v1/2026.findings-eacl.84
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