Future of NLG evaluation

2026-06-26 · Source: Ehud Reiter's Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, short

Summary

The future of Natural Language Generation (NLG) evaluation must become more meaningful and rigorous, moving beyond current practices that often lack experimental rigor and rely heavily on artificial benchmarks. Many NLP experiments suffer from poor design, inappropriate datasets, lack of reproducibility, data contamination, or reward hacking, issues tolerated by the research culture. To address this, the field needs to shift its focus from benchmark scores to real-world effectiveness. This involves adopting impact evaluation, which directly measures changes in key performance indicators (KPIs) from deployed systems. Additionally, qualitative evaluation, using user feedback and error analysis, can provide deeper insights into how people react to NLP technology. Crucially, safety evaluation must prioritize worst-case and adversarial behaviors, as users and society value system safety over marginal performance gains. The author anticipates significant progress by 2030 and widespread adoption of these comprehensive evaluation goals by 2035.

Key takeaway

For MLOps Engineers or AI Scientists deploying NLG systems, relying solely on benchmark scores is insufficient and misleading. You should integrate impact, qualitative, and safety evaluations into your development lifecycle. Measure real-world KPI changes, analyze user feedback for deeper insights, and rigorously test for worst-case and adversarial scenarios. This ensures your systems are not just performant on artificial metrics, but genuinely effective and safe for users.

Key insights

NLG evaluation must shift from artificial benchmarks to rigorous, real-world impact, qualitative, and safety assessments.

Principles

• Experimental rigor is paramount for scientific validity.
• Benchmark scores do not reflect real-world system effectiveness.
• Evaluation should prioritize user insights over numerical scores.

Method

Shift evaluation from benchmarks to real-world effectiveness by conducting impact evaluations (KPI changes), qualitative analyses (user feedback, error analysis), and safety evaluations (worst-case, adversarial scenarios).

In practice

• Measure deployed system impact on user KPIs.
• Analyze user feedback and perform qualitative error analysis.
• Test system behavior in worst-case and adversarial contexts.

Topics

• NLG Evaluation
• Experimental Rigor
• Impact Evaluation
• Qualitative Evaluation
• Safety Evaluation
• NLP Research Culture

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Ehud Reiter's Blog.