arXiv:2512.18859v3 Announce Type: replace Abstract: Generative AI is likely to transform terminology work by creating new opportunities for automation. At the same time, it raises concerns about the future of terminologists and terminological resources, as efficiency pressures may encourage excessive automation based on the perception that human expertise can be replaced by AI. However, large language models remain unreliable for terminological purposes due to errors, hallucinations, and various forms of bias, making terminologists indispensable for ensuring the accuracy and reliability of ter
The proliferation of generative AI models necessitates a re-evaluation of human roles and an understanding of AI's current limitations in specialized fields like terminology work.
A strategic reader should care about the human-AI collaboration paradigm, as it directly impacts efficiency, accuracy, and the future workforce in knowledge-intensive domains, informing investment and policy decisions.
The perception of AI's role shifts from a potential full replacement to an indispensable assistant that requires human oversight, redefining workflow integration and skill development.
- · Terminologists
- · Language service providers
- · AI developers focused on explainability
- · Organizations prioritizing data accuracy
- · Providers of unreliable AI-only solutions
- · Professionals resistant to AI integration
- · Organizations seeking complete human replacement with AI
Increased investment in human-AI collaborative tools and training for specialized tasks.
Development of industry standards and best practices for validating AI-generated content in critical information fields.
A broader redefinition across sectors of where human expertise remains paramount, leading to a more nuanced view of AI's transformative power.
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