RoBiologyDataChoiceQA: A Romanian Dataset for improving Biology understanding of Large Language Models
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Abstract (arXiv)
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.
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References · 10
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2020Measuring Massive Multitask Language Understandingcited by 7,963
- 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
- 2018Datasheets for Datasetscited by 1,386
- 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,356
- 2019PubMedQA: A Dataset for Biomedical Research Question Answeringcited by 1,288
- 2024Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?cited by 71
- 2024Model Equality Testing: Which Model Is This API Serving?cited by 47
- 2023DocTr: Document Transformer for Structured Information Extraction in Documentscited by 18
- 2024RoQLlama: A Lightweight Romanian Adapted Language Modelcited by 7
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- 2024LAB-Bench: Measuring Capabilities of Language Models for Biology Research
- 2024Survey on Reasoning Capabilities and Accessibility of Large Language Models Using Biology-related Questions
- 2024SciKnowEval: Evaluating Multi-level Scientific Knowledge of Large Language Models
- 2025Benchmarking Large Language Models on Multiple Tasks in Bioinformatics NLP with Prompting
- 2023Evaluating the Potential of Leading Large Language Models in Reasoning Biology Questions
- 2024A Survey for Large Language Models in Biomedicine
- 2023Large Language Models are Zero Shot Hypothesis Proposers
- 2026The limits of bio-molecular modeling with large language models : a cross-scale evaluation