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Humans are widely involved in the evaluation of open-ended natural languagegeneration tasks (NLG) that demand creativity, as automatic metrics oftenexhibit weak correlations with human judgments. Large language models (LLMs)recently have emerged as a scalable and cost-effective alternative to humanevaluations. However, both humans and LLMs have limitations, i.e., inherentsubjectivity and unreliable judgments, particularly for open-ended tasks thatrequire adaptable metrics tailored to diverse task requirements. To explore thesynergy between humans and LLM-based evaluators and address the challenges ofexisting inconsistent evaluation criteria in open-ended NLG tasks, we propose aCollaborative Evaluation pipeline CoEval, involving the design of a checklistof task-specific criteria and the detailed evaluation of texts, in which LLMgenerates initial ideation, and then humans engage in scrutiny. We conducted aseries of experiments to investigate the mutual effects between LLMs and humansin CoEval. Results show that, by utilizing LLMs, CoEval effectively evaluateslengthy texts, saving significant time and reducing human evaluation outliers.Human scrutiny still plays a role, revising around 20% of LLM evaluation scoresfor ultimate reliability.

Collaborative Evaluation: Exploring the Synergy of Large Language Models and Humans for Open-ended Generation Evaluation