Continual Training of Language Models for Few-Shot Learning
Open accessAbstract
Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications.Adapting or posttraining an LM using an unlabeled domain corpus can produce even better performance for end-tasks in the domain.This paper proposes the problem of continually extending an LM by incrementally post-train the LM with a sequence of unlabeled domain corpora to expand its knowledge without forgetting its previous skills.The goal is to improve the few-shot end-task learning in these domains.The resulting system is called CPT (Continual Post-Training), which to our knowledge, is the first continual post-training system.Experimental results verify its effectiveness.
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The proposed hybrid architecture demonstrates substantial performance improvements across all benchmark datasets compared to existing state-of-the-art continual learning methods.
“The proposed hybrid architecture demonstrates substantial performance improvements across all benchmark datasets compared to existing state-of-the-art continual learning methods48”
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An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-Tuning
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An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning
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- Authors
- Ke, Z., Lin, H., Shao, Y., Xu, H., Shu, L., & Liu, B.
- Affiliations
- Google (United States) (US)
- Peking University (CN)
- Venue
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Published
- 2022
- Pages
- 10205-10216
- OpenAlex
- W4385567084
- Cited by
- 35
- References
- 10
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 11 · 2023: 6
- FWCI
- 4.01
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Funders
- National Science Foundation (2225427, 1838770, 1910424)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- large language models, few-shot learning, continual learning, domain adaptation, knowledge retention, domain-specific language models
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