1. Self-training Large Language Models through Knowledge Detection
- Author
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Yeo, Wei Jie, Ferdinan, Teddy, Kazienko, Przemyslaw, Satapathy, Ranjan, and Cambria, Erik
- Subjects
Computer Science - Computation and Language - Abstract
Large language models (LLMs) often necessitate extensive labeled datasets and training compute to achieve impressive performance across downstream tasks. This paper explores a self-training paradigm, where the LLM autonomously curates its own labels and selectively trains on unknown data samples identified through a reference-free consistency method. Empirical evaluations demonstrate significant improvements in reducing hallucination in generation across multiple subjects. Furthermore, the selective training framework mitigates catastrophic forgetting in out-of-distribution benchmarks, addressing a critical limitation in training LLMs. Our findings suggest that such an approach can substantially reduce the dependency on large labeled datasets, paving the way for more scalable and cost-effective language model training., Comment: Under review
- Published
- 2024