GPT-4: Evaluating Large Language Models on Pest Management in Agriculture
Core Concepts
GPT-4 demonstrates effectiveness in evaluating and generating pest management advice in agriculture.
Abstract
Abstract:
Introduction to the use of large language models (LLMs) in agriculture for pest management.
Proposal of an innovative approach using GPT-4 to evaluate the quality of text generated by LLMs. Related Work:
Application of LLMs in various domains like finance, medicine, and education.
Exploration of LLMs' potential in agriculture through studies and evaluations. Experiment Design:
Overview of GPT series from OpenAI and FLAN-T5 model from Google used in the experiment.
Baselines established for generating labeled samples for pest scenarios based on expert system data. Experiment Prompting:
Description of zero-shot, few-shot, instruction-based, and self-consistency prompting methods used in the experiment. Results:
Evaluation of linguistic quality and performance metrics across different models and prompting methods using GPT-4. Conclusion:
Summary of findings highlighting the strengths and limitations of different LLMs and prompting methods for pest management suggestions in agriculture. Plans for future improvements.