title: Language Complexity Measurement as a Noisy Zero-Shot Proxy for
    Evaluating LLM Performance
summary: Large Language Models (LLMs) have made significant strides in natural
  language generation but often face challenges in tasks requiring precise
  calculations and structural analysis. This paper investigates the performance
  of state-of-the-art LLMs on language complexity measurement tasks, through the
  computation of the LIX readability metric and Average Dependency Distance
  (ADD). Using Swedish high school and university-level essays, we evaluate the
  models' abilities to compute LIX scores and perform dependency parsing,
  comparing their results to established ground truths. Our findings reveal that
  while all models demonstrate some capacity for these tasks, ChatGPT-o1-mini
  performs most consistently, achieving the highest accuracy in both LIX
  computation and dependency parsing. Additionally, we observe a strong
  significant correlation -0.875 p 0.026 (N=6) between the models' accuracy in
  computing LIX and their overall performance on the Massive Multitask Language
  Understanding (MMLU) benchmark. These results suggest that language complexity
  measurement abilities can serve as a noisy zero-shot proxies for assessing the
  general capabilities of LLMs, providing a practical method for model evaluation
  without the need for extensive benchmarking datasets.
paper_authors._id: 67b435475bff5f34c1ebee1b
paper_authors.name: Birger Moell
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-18T09:30:52.639Z
paper_authors._id: 6033e34a9aa44495c80dd043
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1614079701740-6033e34a9aa44495c80dd043.jpeg
paper_authors.fullname: Birger Moell
paper_authors.type: user
paper_authors.user: birgermoell
paper_authors._id: 67b435475bff5f34c1ebee1c
paper_authors.name: Johan Boye
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-02-18T07:22:48.554Z
paper_authors._id: 65bcbc01d6d0ffbceb8b2e6e
paper_authors.avatarUrl: /avatars/73edb2d6b7b11208439ac88b365079e8.svg
paper_authors.fullname: Johan Boye
paper_authors.type: user
paper_authors.user: jboye
paper_title: Language Complexity Measurement as a Noisy Zero-Shot Proxy for
    Evaluating LLM Performance
paper_summary: Large Language Models (LLMs) have made significant strides in natural
  language generation but often face challenges in tasks requiring precise
  calculations and structural analysis. This paper investigates the performance
  of state-of-the-art LLMs on language complexity measurement tasks, through the
  computation of the LIX readability metric and Average Dependency Distance
  (ADD). Using Swedish high school and university-level essays, we evaluate the
  models' abilities to compute LIX scores and perform dependency parsing,
  comparing their results to established ground truths. Our findings reveal that
  while all models demonstrate some capacity for these tasks, ChatGPT-o1-mini
  performs most consistently, achieving the highest accuracy in both LIX
  computation and dependency parsing. Additionally, we observe a strong
  significant correlation -0.875 p 0.026 (N=6) between the models' accuracy in
  computing LIX and their overall performance on the Massive Multitask Language
  Understanding (MMLU) benchmark. These results suggest that language complexity
  measurement abilities can serve as a noisy zero-shot proxies for assessing the
  general capabilities of LLMs, providing a practical method for model evaluation
  without the need for extensive benchmarking datasets.
paper_ai_summary: Evaluation of state-of-the-art LLMs on language complexity measurement tasks shows ChatGPT-o1-mini performs most consistently, and accuracy in LIX computation correlates significantly with MMLU benchmark performance.
paper_ai_keywords: Large Language Models LIX readability metric Average Dependency Distance dependency parsing Massive Multitask Language Understanding (MMLU) benchmark