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    <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.</abstract>
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%0 Conference Proceedings
%T Self-training Large Language Models through Knowledge Detection
%A Wei Jie, Yeo
%A Ferdinan, Teddy
%A Kazienko, Przemyslaw
%A Satapathy, Ranjan
%A Cambria, Erik
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F wei-jie-etal-2024-self
%X 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.
%U https://aclanthology.org/2024.findings-emnlp.883
%P 15033-15045

Markdown (Informal)

[Self-training Large Language Models through Knowledge Detection](https://aclanthology.org/2024.findings-emnlp.883) (Wei Jie et al., Findings 2024)

• Self-training Large Language Models through Knowledge Detection (Wei Jie et al., Findings 2024)

ACL

• Yeo Wei Jie, Teddy Ferdinan, Przemyslaw Kazienko, Ranjan Satapathy, and Erik Cambria. 2024. Self-training Large Language Models through Knowledge Detection. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 15033–15045, Miami, Florida, USA. Association for Computational Linguistics.