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Reference-Based Analysis of Coherence and Diversity in Open-Ended Text Generation

arXiv · AI, language, vision and robotics · article · Sep 23, 2026 · UTC

Evaluating open-ended text generation involves understanding how different properties of a continuation relate to its perceived quality. We present a reference-based framework for examining coherence and diversity through three perspectives: aligning their evolution with human trajectories, comparing their summaries with a human continuation of the same prompt, and estimating their likelihood under a human reference distribution. Experiments with human quality ratings suggest that diversity-based alignment and mean-based comparisons capture quality-related variation, although the comparisons d

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