Harnessing Large Language Models for Cognitive Assistants in Factories

Abstract

As agile manufacturing expands and workforce mobility increases, the importance of efficient knowledge transfer among factory workers grows. Cognitive Assistants (CAs) with Large Language Models (LLMs), like GPT-3.5, can bridge knowledge gaps and improve worker performance in manufacturing settings. This study investigates the opportunities, risks, and user acceptance of LLM-powered CAs in two factory contexts: textile and detergent production. Several opportunities and risks are identified through a literature review, proof-of-concept implementation, and focus group sessions. Factory representatives raise concerns regarding data security, privacy, and the reliability of LLMs in high-stake environments. By following design guidelines regarding persistent memory, realtime data integration, security, privacy, and ethical concerns, LLMpowered CAs can become valuable assets in manufacturing settings and other industries. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Natural language interfaces; • Computing methodologies → Natural language generation; • Information systems → Users and interactive retrieval; • Applied computing → Industry and manufacturing.

Knowledge sharing in manufacturing using LLM-powered tools: user study and model benchmarking

“…Xia et al ( 2023 ) demonstrated how using in-context learning and injecting task-specific knowledge into an LLM can streamline intelligent planning and control of production processes. Kernan Freire et al ( 2023a ) built a proof of concept for bridging knowledge gaps among workers by utilizing domain-specific texts and knowledge graphs. Wang X. et al ( 2023 ) conducted a systematic test of ChatGPT's responses to 100 questions from course materials and industrial documents.…”