GPT-4 as Evaluator: Evaluating Large Language Models on Pest Management
  in Agriculture
Shanglong Yang Zhipeng Yuan Shunbao Li Ruoling Peng Kang Liu Po Yang
In the rapidly evolving field of artificial intelligence (AI), the
application of large language models (LLMs) in agriculture, particularly in
pest management, remains nascent. We aimed to prove the feasibility by
evaluating the content of the pest management advice generated by LLMs,
including the Generative Pre-trained Transformer (GPT) series from OpenAI and
the FLAN series from Google. Considering the context-specific properties of
agricultural advice, automatically measuring or quantifying the quality of text
generated by LLMs becomes a significant challenge. We proposed an innovative
approach, using GPT-4 as an evaluator, to score the generated content on
Coherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and
Exhaustiveness. Additionally, we integrated an expert system based on crop
threshold data as a baseline to obtain scores for Factual Accuracy on whether
pests found in crop fields should take management action. Each model's score
was weighted by percentage to obtain a final score. The results showed that
GPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories.
Furthermore, the use of instruction-based prompting containing domain-specific
knowledge proved the feasibility of LLMs as an effective tool in agriculture,
with an accuracy rate of 72%, demonstrating LLMs' effectiveness in providing
pest management suggestions.