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

arXiv:2609.28080 · cs.CL · Submitted 2026-09-23 · Read on arXiv

cs.CL

Submitted: 2026-09-23

Updated: 2026-09-23

Code: https://github.com/EstebanGarces/likely_human

Terminology

• Alignment — 29× in this paper · explained in 1 episode
• Ablation — 6× in this paper · explained in 1 episode
• Perplexity — 5× in this paper · explained in 3 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Munich Center for Machine Learning — 2× in this paper
• Aggregation — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Coherence Length — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Empirical Coverage — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Hierarchical Neural Story Generation
• Evaluation of Text Generation: A Survey
• Breaking the Likelihood Trap: Variance-Calibrated Modulation for Large Language Model Decoding
• The Truncation Blind Spot: How Decoding Strategies Systematically Exclude Human-Like Token Choices
• The Curious Case of Neural Text Degeneration
• OPT: Open Pre-trained Transformer Language Models

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