2024/08/28 by Sagar Srinivas Sakhinana, Sakhinana, Sagar Srinivas, Geethan Sannidhi +3
Business, Management and Accounting · Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Business Process Modeling and Analysis #FOS: Computer and information sciences #Machine Learning (cs.LG) #Manufacturing Process and Optimization #Semantic Web and Ontologies #Software Engineering (cs.SE)
paper · pdf · doi:10.48550/arxiv.2408.15866
openalex publication_date 2024/08/28 · openalex created_date 2024/09/22 · openalex updated_date 2026/07/28
The current technology landscape lacks a foundational AI model for solving process engineering calculations. In this work, we introduce a novel autonomous agent framework leveraging Retrieval-Augmented Instruction-Tuning (RAIT) to enhance open, customizable small code language models (SLMs) for these calculations. By combining instruction tuned code SLMs with Retrieval-Augmented Code Generation (RACG) using external tools, the agent generates, debugs, and optimizes code from natural language specifications. Our approach addresses the limitations of the current lack of a foundational AI model for specialized process engineering tasks and offers benefits of explainability, knowledge editing, and cost-effectiveness. Additionally, we curate custom datasets of chemical and process engineering problems and solutions to overcome data scarcity. Experimental results show that our framework matches the performance of large-scale proprietary models on benchmark datasets, proving its effectiveness and usability.