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An NLP-Based Evaluation of LLMs Across Creativity, Factual Accuracy, Open-Ended and Technical Explanations
Research Article | Published: 01 February 2026
ICCK Transactions on Emerging Topics in Artificial Intelligence Volume 3, Issue 2, 2026: 76-85
Volume 3, Issue 2, ICCK Transactions on Emerging Topics in Artificial Intelligence
Volume 3, Issue 2, 2026
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ICCK Transactions on Emerging Topics in Artificial Intelligence, Volume 3, Issue 2, 2026: 76-85

Open Access | Research Article | 01 February 2026
An NLP-Based Evaluation of LLMs Across Creativity, Factual Accuracy, Open-Ended and Technical Explanations
by
Qazi Novera Tansue Nasa Qazi Novera Tansue Nasa 1 *
Ashik Chandra Das Ashik Chandra Das 1
1 Institute of Computer Science, University of Potsdam, Potsdam 14476, Germany
* Corresponding Author: Qazi Novera Tansue Nasa, [email protected]
DOI: 10.62762/TETAI.2025.264517
ARK: ark:/57805/tetai.2025.264517
Received: 10 November 2025, Accepted: 01 December 2025, Published: 01 February 2026  
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Abstract
The rapid advancement of AI-based language models has transformed the field of Natural Language Processing (NLP) into a powerful tool for text generation. This study evaluates the performance of models in different categories such as factual accuracy, creative writing, open-ended writing, and technical explanation. We have considered three popular and advanced large language models (LLMs) for this analysis. To quantify their performance, we have applied a combination of statistical and linguistic metrics. We have used Dale-Chall to analyze the readability score of the responses. For lexical diversity, we have used the type-token ratio technique. In addition, a cosine similarity with TF-IDF is used for semantic similarity. Furthermore, sentiment polarity and grammatical correctness are also analyzed. Moreover, we have conducted an F-test to determine whether the differences in performance among the LLMs are statistically significant (p < 0.05). We have found minimal differences between LLMs, with ChatGPT showing slightly better performance compared to the others.
Graphical Abstract
An NLP-Based Evaluation of LLMs Across Creativity, Factual Accuracy, Open-Ended and Technical Explanations
Keywords
LLMs evaluation
NLP
ChatGPT
Gemini
DeepSeek
ANOVA
Data Availability Statement
The data and code supporting the findings of this study are publicly available at the following repository: https://github.com/acdas10/NLP-based-LLM-analysis
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
AI Use Statement
The authors declare that no generative AI was used in the preparation of this manuscript.
Ethical Approval and Consent to Participate
Not applicable.
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Cite This Article
APA Style
Nasa, Q. N. T., & Das, A. C. (2026). An NLP-Based Evaluation of LLMs Across Creativity, Factual Accuracy, Open-Ended and Technical Explanations. ICCK Transactions on Emerging Topics in Artificial Intelligence, 3(2), 76–85. https://doi.org/10.62762/TETAI.2025.264517
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TY  - JOUR
AU  - Nasa, Qazi Novera Tansue
AU  - Das, Ashik Chandra
PY  - 2026
DA  - 2026/02/01
TI  - An NLP-Based Evaluation of LLMs Across Creativity, Factual Accuracy, Open-Ended and Technical Explanations
JO  - ICCK Transactions on Emerging Topics in Artificial Intelligence
T2  - ICCK Transactions on Emerging Topics in Artificial Intelligence
JF  - ICCK Transactions on Emerging Topics in Artificial Intelligence
VL  - 3
IS  - 2
SP  - 76
EP  - 85
DO  - 10.62762/TETAI.2025.264517
UR  - https://www.icck.org/article/abs/TETAI.2025.264517
KW  - LLMs evaluation
KW  - NLP
KW  - ChatGPT
KW  - Gemini
KW  - DeepSeek
KW  - ANOVA
AB  - The rapid advancement of AI-based language models has transformed the field of Natural Language Processing (NLP) into a powerful tool for text generation. This study evaluates the performance of models in different categories such as factual accuracy, creative writing, open-ended writing, and technical explanation. We have considered three popular and advanced large language models (LLMs) for this analysis. To quantify their performance, we have applied a combination of statistical and linguistic metrics. We have used Dale-Chall to analyze the readability score of the responses. For lexical diversity, we have used the type-token ratio technique. In addition, a cosine similarity with TF-IDF is used for semantic similarity. Furthermore, sentiment polarity and grammatical correctness are also analyzed. Moreover, we have conducted an F-test to determine whether the differences in performance among the LLMs are statistically significant (p < 0.05). We have found minimal differences between LLMs, with ChatGPT showing slightly better performance compared to the others.
SN  - 3068-6652
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{Nasa2026An,
  author = {Qazi Novera Tansue Nasa and Ashik Chandra Das},
  title = {An NLP-Based Evaluation of LLMs Across Creativity, Factual Accuracy, Open-Ended and Technical Explanations},
  journal = {ICCK Transactions on Emerging Topics in Artificial Intelligence},
  year = {2026},
  volume = {3},
  number = {2},
  pages = {76-85},
  doi = {10.62762/TETAI.2025.264517},
  url = {https://www.icck.org/article/abs/TETAI.2025.264517},
  abstract = {The rapid advancement of AI-based language models has transformed the field of Natural Language Processing (NLP) into a powerful tool for text generation. This study evaluates the performance of models in different categories such as factual accuracy, creative writing, open-ended writing, and technical explanation. We have considered three popular and advanced large language models (LLMs) for this analysis. To quantify their performance, we have applied a combination of statistical and linguistic metrics. We have used Dale-Chall to analyze the readability score of the responses. For lexical diversity, we have used the type-token ratio technique. In addition, a cosine similarity with TF-IDF is used for semantic similarity. Furthermore, sentiment polarity and grammatical correctness are also analyzed. Moreover, we have conducted an F-test to determine whether the differences in performance among the LLMs are statistically significant (p < 0.05). We have found minimal differences between LLMs, with ChatGPT showing slightly better performance compared to the others.},
  keywords = {LLMs evaluation, NLP, ChatGPT, Gemini, DeepSeek, ANOVA},
  issn = {3068-6652},
  publisher = {Institute of Central Computation and Knowledge}
}
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CC BY Copyright © 2026 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
ICCK Transactions on Emerging Topics in Artificial Intelligence

ICCK Transactions on Emerging Topics in Artificial Intelligence

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