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<p>This study proposes a fine-tuning method based on a large language model (LLM), aiming to improve the model’s generalization and accuracy in the task of extracting aquaculture medication. Model’s ability to recognize disease prevention and treatment medication events has been enhanced by constructing an instruction tuning (IT) dataset. ChatGLM4-9B model was further optimized using the rank-stable scaling factor with low-rank fine-tuning (rsLoRA) technique and hyper-parameters were adjusted in real-time using a dynamic parameter fine-tuning strategy. Experimental results show that the RSI-Tune method reaches more than 95% on several evaluation metrics, significantly outperforming traditional LoRA and DoRA methods. This research not only provides technical support for intelligent management of aquaculture but also offers new insights for the development of smart agriculture.</p>

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Base on ChatGLM extraction of medication events in aquaculture with few samples

  • 期刊论文

  • 发表日期：

    2025年1月1日

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  • Zhenglin Li,
  • Sijia Zhang,
  • Zongshi An

摘要

This study proposes a fine-tuning method based on a large language model (LLM), aiming to improve the model’s generalization and accuracy in the task of extracting aquaculture medication. Model’s ability to recognize disease prevention and treatment medication events has been enhanced by constructing an instruction tuning (IT) dataset. ChatGLM4-9B model was further optimized using the rank-stable scaling factor with low-rank fine-tuning (rsLoRA) technique and hyper-parameters were adjusted in real-time using a dynamic parameter fine-tuning strategy. Experimental results show that the RSI-Tune method reaches more than 95% on several evaluation metrics, significantly outperforming traditional LoRA and DoRA methods. This research not only provides technical support for intelligent management of aquaculture but also offers new insights for the development of smart agriculture.