PetQA: Benchmarking Veterinary Knowledge and Clinical Reasoning
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Computer Science > Computation and Language
arXiv:2609.04598 (cs)
[Submitted on 4 Sep 2026]
Title:PetQA: Benchmarking Veterinary Knowledge and Clinical Reasoning
View a PDF of the paper titled PetQA: Benchmarking Veterinary Knowledge and Clinical Reasoning, by Taegyun Kim and 5 other authors
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Abstract:We introduce PetQA, a Korean long-form question-answering (QA) benchmark for evaluating veterinary knowledge and clinical reasoning in large language models (LLMs) and large vision-language models (LVLMs). PetQA contains 10,076 text-only and 8,751 multimodal QA pairs derived from real-world questions about dogs and cats, paired with answers from expert veterinarians. Its test split, PetQA-Bench, further includes annotations for question types and clinical conditions. We evaluate eighteen models using ROUGE, BERTScore, and LLM-as-a-judge metrics for factuality and helpfulness under three settings: zero-shot inference, retrieval-augmented generation (RAG), and supervised fine-tuning (SFT). The benchmarking results provide an overview of the strengths and limitations of current models in addressing veterinary clinical queries and highlight the need for more effective adaptation methods to develop clinically reliable AI systems for veterinary care. To facilitate broader use, we additionally provide translated versions of PetQA-Bench in five languages.
| Comments: | EMNLP 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2609.04598 [cs.CL] |
| (or arXiv:2609.04598v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.04598
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled PetQA: Benchmarking Veterinary Knowledge and Clinical Reasoning, by Taegyun Kim and 5 other authors
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