Self-training Large Language Models through Knowledge Detection
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.
Paper Type: Long
Research Area: Efficient/Low-Resource Methods for NLP
Research Area Keywords: self-training, knowledge parsing, LLM
Contribution Types: Approaches to low-resource settings
Languages Studied: english
Submission Number: 2505
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