@inproceedings{nainia-etal-2025-f,
    title = "{F}-{L}o{RA}-{QA}: Finetuning {LL}a{MA} Models with Low-Rank Adaptation for {F}rench Botanical Question Generation and Answering",
    author = "Nainia, Ayoub  and
      Vignes-Lebbe, R{\'e}gine  and
      Mousannif, Hajar  and
      Zahir, Jihad",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-1.91/",
    pages = "787--796",
    abstract = "Despite recent advances in large language models (LLMs), most question-answering (QA) systems remain English-centric and poorly suited to domain-specific scientific texts. This linguistic and domain bias poses a major challenge in botany, where a substantial portion of knowledge is documented in French. We introduce F-LoRA-QA, a fine-tuned LLaMA-based pipeline for French botanical QA, leveraging Low-Rank Adaptation (LoRA) for efficient domain adaptation. We construct a specialized dataset of 16,962 question-answer pairs extracted from scientific flora descriptions and fine-tune LLaMA models to retrieve structured knowledge from unstructured botanical texts. Expert-based evaluation confirms the linguistic quality and domain relevance of generated answers. Compared to baseline LLaMA models, F-LoRA-QA achieves a 300{\%} BLEU score increase, 70{\%} ROUGE-1 F1 gain, +16.8{\%} BERTScore F1, and Exact Match improvement from 2.01{\%} to 23.57{\%}. These results demonstrate the effectiveness of adapting LLMs to low-resource scientific domains and highlight the potential of our approach for automated trait extraction and biodiversity data structuring."
}