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Benchmarking Linguistic Diversity of Large Language Models

Paper · 2025 · Yanzhu Guo, Guokan Shang and Chloé Clavel

Summary

LLM evaluation usually focuses on task accuracy, factuality or safety. Whether model text is as varied as human writing gets little systematic attention. The authors build a corpus-level framework that scores generated text on three fairly independent axes: lexical, syntactic and semantic diversity. Diversity is measured across inputs, with one output per prompt, rather than among several samples for the same prompt. Using this framework, they assess six well-known LLMs across five natural language generation tasks, focusing especially on story continuation because creativity is most important in that setting. This includes a case study comparing dependency-tree distributions in human and machine stories. They then vary training stage, decoding temperature, prompt wording, model size and quantization. The main finding is that, despite fluent output, LLMs clearly lag behind human richness in creative writing. Design choices also shift each diversity dimension differently. As synthetic text spreads online, this points to a risk of homogenized language. The experiments are English-only, but the method needs only a dependency parser and sentence embeddings, so it could be ported to other languages such as Ukrainian.

Key points

  • Instruction tuning raises lexical diversity but narrows syntactic and semantic diversity.
  • Higher decoding temperature increases lexical diversity at a small cost in BERTScore relevance and leaves semantic diversity unchanged. Syntactic diversity drops for OLMo and Falcon, which were trained on far fewer tokens.
  • Adding "be as creative as possible" to the WritingPrompts instruction barely changes any diversity score. Prompt engineering alone is therefore unlikely to fix the problem.
  • In Qwen2.5 models from 0.5B to 32B, lexical diversity grows with size and semantic diversity stays flat. Syntactic diversity peaks at 7B, so larger models are not automatically more diverse.
  • 4-bit bitsandbytes quantization (versus bf16) lowers lexical diversity, mostly in small models, and syntactic diversity, mostly in large ones. Semantic diversity is unaffected. Code is at github.com/YanzhuGuo/llm-diversity.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🔬Institut national de recherche en sciences et technologies du numériqueLe Chesnay-Rocquencourt · France84 works · 25,414 citations🔬ALMANACH: Modélisation et analyse linguistique automatique et humanités computationnellesParis · France19 works · 6,924 citations

Also on the byline: Institut français de la mode.

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

Cite

Cite as Yanzhu Guo, Guokan Shang and Chloé Clavel. 2025. Benchmarking Linguistic Diversity of Large Language Models. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/tacl.a.47

@article{guo2025benchmarking,
  title = {{Benchmarking Linguistic Diversity of Large Language Models}},
  author = {Yanzhu Guo, Guokan Shang and Chloé Clavel},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2025},
  doi = {10.1162/tacl.a.47},
  url = {https://doi.org/10.1162/tacl.a.47}
}

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