Rational Intelligence Model: Overcoming Data Handling Limitations in LLMs

Many human-computer interactions and automated processes are dependent on data. We need to correctly store data and retrieve data to support these processes. Large Language Models (LLMs) have made significant progress in comprehension and reasoning. However, we have found that their ability to handle professional data is weak, which significantly limits their applications in automated processes and professional applications. These scenarios often involve a large number of similar data entries, which are challenging for LLMs such as ChatGPT-4 Omni. In this paper, we propose a Rational Intelligence Model that comprehends human experts' knowledge of data structure and process requirements of data, automatically extracts data from conversations with end users, and effectively stores the data and retrieves it for supporting the interaction and process. Experiments show that ChatGPT-4o can achieve 0% error in simple queries, 2.6% errors when data entries are out of order, and 38% errors when the queries are reasonably complex. However, with our proposed Rational Intelligence Model (RIM), we can achieve 0% error rate in all tests. RIM fundamentally changes software engineering and expert system development approaches. Instead of having a software engineer understand expert knowledge of data processing, this is now achieved by RIM, which means it is much more flexible, lower in cost, and requires much less development time.