arXiv:2605.23715v1 Announce Type: new Abstract: Natural Language Generation (NLG) evaluation has changed dramatically since 1990, and will continue to evolve in the future. In 1990, when NLG had close ties to linguistics, there was very little formal experimental evaluation in the modern sense. In 2026, when NLG is closely linked to machine learning, experimental evaluation is expected and indeed fundamental to research. Many evaluation techniques were developed over this period, including most recently LLM-as-Judge. I expect NLG evaluation will continue to evolve in the future. In particular,
The rapid advancement and integration of generative AI models, particularly large language models, necessitate a re-evaluation of established evaluation paradigms in Natural Language Generation research.
The evolution of NLG evaluation directly impacts the reliability and progress of AI systems, crucial for deploying trustworthy and effective AI applications across industries.
The shift towards methods like LLM-as-Judge indicates a fundamental change in how AI system quality is assessed, moving beyond traditional metrics to more holistic and AI-native approaches.
- · AI researchers focusing on evaluation methodologies
- · Developers of meta-evaluation tools
- · Companies investing in advanced AI quality assurance
- · Researchers relying solely on outdated evaluation metrics
- · AI systems that perform poorly under sophisticated evaluation
- · Traditional, human-intensive evaluation services
The adoption of new evaluation techniques will lead to more robust and accurate comparisons of NLG models.
Improved evaluation will accelerate the development of higher-performing and more reliable generative AI systems.
This could eventually enable more autonomous and complex AI agentic systems as their outputs become more consistently verifiable.
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