Illusions of the Gold Standard: A Large-scale Analysis of Human Evaluation Protocols for Long-form Text Generation
arXiv:2606.07936v1 Announce Type: cross Abstract: Human evaluation plays a critical role in assessing the quality of generated text. However, the reliability and reproducibility of these evaluations depend on transparent and well-documented protocols -- details that are frequently missing in current practice. In this work, we conduct a large-scale analysis of human evaluation protocols for evaluating long-form generation tasks in *CL conference publications from 2023--2025, including a full manual review of 284 papers and LLM-assisted analysis for another 1.8k+ papers. We define a set of 20 re
This large-scale analysis addresses a critical and growing need for reliable human evaluation in the rapidly expanding field of long-form text generation, especially as AI models become more sophisticated.
Reliable evaluation protocols are fundamental to the progress and trustworthiness of AI in generating complex text, directly impacting the development and adoption of AI agents and applications.
The research highlights deficiencies in current human evaluation practices, pushing researchers and developers towards more transparent, reproducible, and rigorous methodologies for AI text generation.
- · AI evaluation methodology researchers
- · Developers of robust AI text generation models
- · Users and consumers of AI-generated content
- · AI models relying on poorly evaluated metrics
- · Researchers using opaque evaluation protocols
- · Systems lacking auditable performance benchmarks
Improved human evaluation standards will lead to more robust and trustworthy long-form AI text generation.
Better evaluation metrics will accelerate the development of more capable AI agents able to handle complex generation tasks.
Increased confidence in AI-generated text could lead to broader integration of AI agents into critical workflows, potentially displacing traditional human content creation in various domains.
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Read at arXiv cs.AI