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Introducing ChatSQC: Enhancing Statistical Quality Control with Augmented AI

Fadel M. Megahed, Ying-Ju Chen, Inez Zwetsloot, Sven Knoth, Douglas C. Montgomery, L. Allison Jones-Farmer

Abstract

We introduce ChatSQC, an innovative chatbot system that combines the power of OpenAI's Large Language Models (LLM) with a specific knowledge base in Statistical Quality Control (SQC). Our research focuses on enhancing LLMs using specific SQC references, shedding light on how data preprocessing parameters and LLM selection impact the quality of generated responses. By illustrating this process, we hope to motivate wider community engagement to refine LLM design and output appraisal techniques. We also highlight potential research opportunities within the SQC domain that can be facilitated by leveraging ChatSQC, thereby broadening the application spectrum of SQC. A primary goal of our work is to provide a template and proof-of-concept on how LLMs can be utilized by our community. To continuously improve ChatSQC, we ask the SQC community to provide feedback, highlight potential issues, request additional features, and/or contribute via pull requests through our public GitHub repository. Additionally, the team will continue to explore adding supplementary reference material that would further improve the contextual understanding of the chatbot. Overall, ChatSQC serves as a testament to the transformative potential of AI within SQC, and we hope it will spur further advancements in the integration of AI in this field.

Introducing ChatSQC: Enhancing Statistical Quality Control with Augmented AI

Abstract

We introduce ChatSQC, an innovative chatbot system that combines the power of OpenAI's Large Language Models (LLM) with a specific knowledge base in Statistical Quality Control (SQC). Our research focuses on enhancing LLMs using specific SQC references, shedding light on how data preprocessing parameters and LLM selection impact the quality of generated responses. By illustrating this process, we hope to motivate wider community engagement to refine LLM design and output appraisal techniques. We also highlight potential research opportunities within the SQC domain that can be facilitated by leveraging ChatSQC, thereby broadening the application spectrum of SQC. A primary goal of our work is to provide a template and proof-of-concept on how LLMs can be utilized by our community. To continuously improve ChatSQC, we ask the SQC community to provide feedback, highlight potential issues, request additional features, and/or contribute via pull requests through our public GitHub repository. Additionally, the team will continue to explore adding supplementary reference material that would further improve the contextual understanding of the chatbot. Overall, ChatSQC serves as a testament to the transformative potential of AI within SQC, and we hope it will spur further advancements in the integration of AI in this field.
Paper Structure (26 sections, 6 equations, 6 figures, 6 tables)

This paper contains 26 sections, 6 equations, 6 figures, 6 tables.

Table of Contents

  1. Introduction
  2. The Construction of ChatSQC
  3. Material Extraction and Integration Process
  4. Extracting Reference Book Materials for ChatSQC-Basic
  5. Extracting Research Papers for ChatSQC-Research
  6. Offline Preprocessing
  7. The Online Chatting Interface
  8. The Hosting/Deployment of ChatSQC
  9. Evaluation of ChatSQC
  10. Study design
  11. ChatSQC-Basic Responses
  12. Responses to Ungrounded Prompts
  13. ChatSQC-Research Responses
  14. Overall Evaluation
  15. Discussion
  16. ...and 11 more sections

Figures (6)

  • Figure 1: Flowchart of the preprocessing and chat interface creation process. The colors in the arrows correspond to the four phases highlighted in the paragraph above.
  • Figure 2: Our custom system prompts for the two modes of ChatSQC.
  • Figure 3: Screenshot of ChatSQC-Basic where the used prompted the question "Who is Walter A. Shewhart?" and its response. This screenshot is only provided to highlight the functionalities of our app and how it provides some features that are not available by default in (most) existing Chat models.
  • Figure 4: Response of ChatSQC to Prompt 9.
  • Figure 5: Response of ChatSQC to Prompt 10.
  • ...and 1 more figures