Large language models for generating rules, yay or nay?

• URL: http://arxiv.org/abs/2406.06835v1
• Date: Mon, 10 Jun 2024 22:44:25 GMT
• Title: Large language models for generating rules, yay or nay?
• Authors: Shangeetha Sivasothy, Scott Barnett, Rena Logothetis, Mohamed Abdelrazek, Zafaryab Rasool, Srikanth Thudumu, Zac Brannelly,
• Abstract summary: We present a novel approach that leverages Large Language Models (LLMs) as a potential world model to accelerate the engineering of software systems. This approach involves using LLMs to generate logic rules, which can then be reviewed and informed by SMEs before deployment. Our experiments show that 1) LLMs have a world model that bootstraps implementation, 2) LLMs generated less number of rules compared to experts, and 3) LLMs do not have the capacity to generate thresholds for each rule.
• Score: 1.773865951534232
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Engineering safety-critical systems such as medical devices and digital health intervention systems is complex, where long-term engagement with subject-matter experts (SMEs) is needed to capture the systems' expected behaviour. In this paper, we present a novel approach that leverages Large Language Models (LLMs), such as GPT-3.5 and GPT-4, as a potential world model to accelerate the engineering of software systems. This approach involves using LLMs to generate logic rules, which can then be reviewed and informed by SMEs before deployment. We evaluate our approach using a medical rule set, created from the pandemic intervention monitoring system in collaboration with medical professionals during COVID-19. Our experiments show that 1) LLMs have a world model that bootstraps implementation, 2) LLMs generated less number of rules compared to experts, and 3) LLMs do not have the capacity to generate thresholds for each rule. Our work shows how LLMs augment the requirements' elicitation process by providing access to a world model for domains.

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