| Title: |
Using LLM-Generated Draft Replies to Support Human Experts in Responding to Stakeholder Inquiries in Maritime Industry: A Real-World Case Study of Industrial AI |
| Authors: |
Bach, Tita Alissa, Babic, Aleksandar, Park, Narae, Sporsem, Tor, Ulfsnes, Rasmus, Smith-Meyer, Henrik, Skeie, Torkel |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Human-Computer Interaction |
| More Details: |
The maritime industry requires effective communication among diverse stakeholders to address complex, safety-critical challenges. Industrial AI, including Large Language Models (LLMs), has the potential to augment human experts' workflows in this specialized domain. Our case study investigated the utility of LLMs in drafting replies to stakeholder inquiries and supporting case handlers. We conducted a preliminary study (observations and interviews), a survey, and a text similarity analysis (LLM-as-a-judge and Semantic Embedding Similarity). We discover that while LLM drafts can streamline workflows, they often require significant modifications to meet the specific demands of maritime communications. Though LLMs are not yet mature enough for safety-critical applications without human oversight, they can serve as valuable augmentative tools. Final decision-making thus must remain with human experts. However, by leveraging the strengths of both humans and LLMs, fostering human-AI collaboration, industries can increase efficiency while maintaining high standards of quality and precision tailored to each case.
Comment: These authors share the first authorship: Tita Alissa Bach (1), Aleksandar Babic (1), Narae Park (1) |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2412.12732 |
| Accession Number: |
edsarx.2412.12732 |
| Database: |
arXiv |
