Analysis and prediction in SCR experiments using GPT-4 with an effective chain-of-thought prompting strategy
Muyu Lu, Fengyu Gao, Xiaolong Tang, Linjiang Chen
Abstract
This study explores the use of large language models (LLMs) in interpreting and predicting experimental outcomes based on given experimental variables, leveraging the human-like reasoning and inference capabilities of LLMs, using selective catalytic reduction of NO x with NH 3 as a case study. We implement the chain of thought (CoT) concept to formulate logical steps for uncovering connections within the data, introducing an "Ordered-and-Structured" CoT (OSCoT) prompting strategy. We compare the OSCoT strategy with the more conventional "One-Pot" CoT (OPCoT) approach and with human experts. We demonstrate that GPT-4, equipped with this new OSCoT prompting strategy, outperforms the other two settings and accurately predicts experimental outcomes and provides intuitive reasoning for its predictions.