评测大模型对动物知识的掌握程度，发现其在生物多样性领域存在系统性短板。
BAGEL: Benchmarking Animal Knowledge Expertise in Language Models
- 基于科学文献与数据库构建闭卷问答集，统一评估模型动物知识
- 覆盖分类、形态、分布等7类动物知识，支持细粒度分析
- 适合研究模型在生物多样性任务中的泛化能力与可靠性
大型语言模型在通用知识和推理基准上表现优异，但其在统一闭卷评估协议下对专业动物知识的掌握程度仍不明确。我们提出BAGEL，一个用于评估语言模型动物知识专长的基准。BAGEL源自多样化的科学与参考资源，包括bioRxiv、Global Biotic Interactions、Xeno-canto和Wikipedia，结合人工筛选示例与自动生成的闭卷问答对构建。该基准涵盖动物知识的多个方面：分类学、形态学、栖息地、行为、发声、地理分布及物种互作。通过聚焦闭卷评估，BAGEL在推理时无需外部检索即可测量模型的动物相关知识。此外，该基准支持跨来源领域、分类群和知识类别进行细粒度分析，可更精确刻画模型优势与系统性失败模式。BAGEL为研究语言模型在特定领域知识泛化问题提供了新测试平台，并有助于提升其在生物多样性相关应用中的可靠性。
原文摘要 · Abstract (English)
Large language models have shown strong performance on broad-domain knowledge and reasoning benchmarks, but it remains unclear how well language models handle specialized animal-related knowledge under a unified closed-book evaluation protocol. We introduce BAGEL, a benchmark for evaluating animal knowledge expertise in language models. BAGEL is constructed from diverse scientific and reference sources, including bioRxiv, Global Biotic Interactions, Xeno-canto, and Wikipedia, using a combination of curated examples and automatically generated closed-book question-answer pairs. The benchmark covers multiple aspects of animal knowledge, including taxonomy, morphology, habitat, behavior, vocalization, geographic distribution, and species interactions. By focusing on closed-book evaluation, BAGEL measures animal-related knowledge of models without external retrieval at inference time. BAGEL further supports fine-grained analysis across source domains, taxonomic groups, and knowledge categories, enabling a more precise characterization of model strengths and systematic failure modes. Our benchmark provides a new testbed for studying domain-specific knowledge generalization in language models and for improving their reliability in biodiversity-related applications.
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