Neural probabilistic models are key to natural language generation, yet the most probable outputs are not always the most qualitative. This paper studies the probability-quality paradox where mode-seeking decoding methods yield unnatural language, while stochastic methods generate more human-like text.

Key insights:

• High-quality text should match the entropy of natural language distributions.
• Quality declines when the information content deviates significantly from this entropy.
• Empirical evidence indicates that the model’s entropy aligns with higher text quality.

The paper suggests that achieving a balance in information content is critical for natural language generation. This observation could influence the design of decoding algorithms and evaluation metrics for language models.

Explore the full discussion at The Probability-Quality Paradox in Neural Probabilistic Models.