Retrieval-Augmented Instruction Tuning for Automated Process Engineering Calculations : A Tool-Chaining Problem-Solving Framework with Attributable Reflection
<|reference_start|>Retrieval-Augmented Instruction Tuning for Automated Process Engineering Calculations : A Tool-Chaining Problem-Solving Framework with Attributable Reflection: 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.<|reference_end|>
@article{sakhinana2024retrieval-augmented,
    title={Retrieval-Augmented Instruction Tuning for Automated Process Engineering
  Calculations : A Tool-Chaining Problem-Solving Framework with Attributable
  Reflection},
    author={Sagar Srinivas Sakhinana, Geethan Sannidhi, and Venkataramana Runkana},
    journal={arXiv preprint arXiv:2408.15866},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15866},
    primaryClass={cs.SE cs.AI cs.LG}
}