RoBiologyDataChoiceQA: A Romanian Dataset for improving Biology understanding of Large Language Models
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914067365494784 |
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| author | Ghinea, Dragos-Dumitru Corbeanu, Adela-Nicoleta Dumitran, Adrian-Marius |
| author_facet | Ghinea, Dragos-Dumitru Corbeanu, Adela-Nicoleta Dumitran, Adrian-Marius |
| contents | In recent years, large language models (LLMs) have demonstrated significant potential across various natural language processing (NLP) tasks. However, their performance in domain-specific applications and non-English languages remains less explored. This study introduces a novel Romanian-language dataset for multiple-choice biology questions, carefully curated to assess LLM comprehension and reasoning capabilities in scientific contexts. Containing approximately 14,000 questions, the dataset provides a comprehensive resource for evaluating and improving LLM performance in biology.
We benchmark several popular LLMs, analyzing their accuracy, reasoning patterns, and ability to understand domain-specific terminology and linguistic nuances. Additionally, we perform comprehensive experiments to evaluate the impact of prompt engineering, fine-tuning, and other optimization techniques on model performance. Our findings highlight both the strengths and limitations of current LLMs in handling specialized knowledge tasks in low-resource languages, offering valuable insights for future research and development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25813 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RoBiologyDataChoiceQA: A Romanian Dataset for improving Biology understanding of Large Language Models Ghinea, Dragos-Dumitru Corbeanu, Adela-Nicoleta Dumitran, Adrian-Marius Computation and Language Machine Learning In recent years, large language models (LLMs) have demonstrated significant potential across various natural language processing (NLP) tasks. However, their performance in domain-specific applications and non-English languages remains less explored. This study introduces a novel Romanian-language dataset for multiple-choice biology questions, carefully curated to assess LLM comprehension and reasoning capabilities in scientific contexts. Containing approximately 14,000 questions, the dataset provides a comprehensive resource for evaluating and improving LLM performance in biology. We benchmark several popular LLMs, analyzing their accuracy, reasoning patterns, and ability to understand domain-specific terminology and linguistic nuances. Additionally, we perform comprehensive experiments to evaluate the impact of prompt engineering, fine-tuning, and other optimization techniques on model performance. Our findings highlight both the strengths and limitations of current LLMs in handling specialized knowledge tasks in low-resource languages, offering valuable insights for future research and development. |
| title | RoBiologyDataChoiceQA: A Romanian Dataset for improving Biology understanding of Large Language Models |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2509.25813 |