arXiv:2605.27268v1 Announce Type: new Abstract: Modern Large Language Models (LLMs) are often criticized for producing repetitive and homogeneous text, despite possessing vast latent vocabularies. While previous research has focused on model knowledge and training data, we investigate the role of decoding mechanics in suppressing linguistic diversity. We introduce the Word Coverage Score (WCS), a metric that quantifies the extent to which contextually appropriate human vocabulary is mathematically pruned by standard sampling filters (e.g., Top-$p$, Top-$k$, and Min-$p$). Rather than assessing
The paper addresses a growing criticism of LLMs related to repetitive output, highlighting a current frontier in improving AI's practical utility.
Improving lexical diversity and reducing homogeneity in LLM output is critical for creating more engaging, human-like, and versatile AI applications, impacting various industries that leverage LLM technology.
The introduction of the Word Coverage Score provides a new, quantifiable metric to assess and improve the linguistic diversity of LLM outputs, shifting focus beyond just knowledge and training data to decoding mechanics.
- · AI developers
- · Content creators
- · Language model users
- · AI research community
- · Developers ignoring output quality
- · Homogeneous content platforms
LLMs will produce more diverse and less repetitive text, enhancing user experience and application versatility.
New optimization techniques for LLMs will emerge, focusing on sampling methods to maximize linguistic diversity while maintaining coherence.
The perceived 'creativity' and general intelligence of LLMs will increase, potentially accelerating their adoption in highly nuanced creative and strategic roles.
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