Large language models can help boost food production, but be mindful of their risks

TL;DR

Large language models have the potential to improve food production efficiency and innovation, but pose risks such as misinformation, data privacy issues, and job threats, requiring careful policy frameworks for responsible use.

Contribution

This paper highlights the opportunities and risks of adopting large language models in agriculture, emphasizing the need for responsible frameworks and guidelines.

Findings

LLMs can enhance agricultural efficiency and innovation.

Risks include misinformation and data privacy concerns.

Policy frameworks are needed for responsible adoption.

Abstract

Coverage of ChatGPT-style large language models (LLMs) in the media has focused on their eye-catching achievements, including solving advanced mathematical problems and reaching expert proficiency in medical examinations. But the gradual adoption of LLMs in agriculture, an industry which touches every human life, has received much less public scrutiny. In this short perspective, we examine risks and opportunities related to more widespread adoption of language models in food production systems. While LLMs can potentially enhance agricultural efficiency, drive innovation, and inform better policies, challenges like agricultural misinformation, collection of vast amounts of farmer data, and threats to agricultural jobs are important concerns. The rapid evolution of the LLM landscape underscores the need for agricultural policymakers to think carefully about frameworks and guidelines that ensure the responsible use of LLMs in food production before these technologies become so ingrained that policy intervention becomes challenging.