Automatic Toxicity Evaluation for Human-LLM Conversations in Flexible Manufacturing System With Duplex Fine-Tuned LLMs

Bo Wang, Chao Wang, Zan Zhou, Yi Sun, Shujie Yang, Yonghui Huang, Yuning Cui, Yuchen Wang, Yasser D. Al-Otaibi, Ali Kashif Bashir, Changqiao Xu

Abstract: Flexible manufacturing systems (FMS), empowered by the Industrial Internet of Things (IIoT), have become a cornerstone of Industry 6.0 by enabling dynamic production adaptation, real-time equipment monitoring, and intelligent scheduling. As these systems increasingly incorporate large language models (LLMs) to support functions such as knowledge querying, decision assistance, and predictive maintenance, ensuring the safety and reliability of human-LLM conversations has become a pressing concern. Specifically, LLMs may generate toxic, biased, or privacy-violating outputs when interacting with sensitive IIoT data and production logic, potentially compromising operational safety. To address this challenge, we propose AugLLMSen, an automated toxicity evaluation framework tailored to the IIoT-driven FMS context. AugLLMSen integrates a question automatic expansion mechanism (Q-Judge) and an output toxicity evaluation model (O-Judge) into a closed-loop pipeline, enabling large-scale assessment of LLM safety across diverse industrial scenarios. Experimental results on open- and closed-source LLMs demonstrate the effectiveness and accuracy of our approach in identifying toxic responses and guiding safe deployment of LLMs in flexible manufacturing environments.

External IDs:dblp:journals/iotj/WangWZSYHCWABX25