Self-training Large Language Models through Knowledge Detection
<|reference_start|>Self-training Large Language Models through Knowledge Detection: 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.<|reference_end|>
@article{yeo2024self-training,
    title={Self-training Large Language Models through Knowledge Detection},
    author={Wei Jie Yeo, Teddy Ferdinan, Przemyslaw Kazienko, Ranjan Satapathy,
  Erik Cambria},
    journal={arXiv preprint arXiv:2406.11275},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11275},
    primaryClass={cs.CL}
}