BAGEL: Benchmarking Animal Knowledge Expertise in Language Models

2026-04-17 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

BAGEL is a new benchmark designed to evaluate the specialized animal knowledge expertise of large language models (LLMs) using a unified closed-book evaluation protocol. Constructed from diverse scientific and reference sources such as bioRxiv, Global Biotic Interactions, Xeno-canto, and Wikipedia, BAGEL includes both curated examples and automatically generated question-answer pairs. The benchmark assesses various aspects of animal knowledge, including taxonomy, morphology, habitat, behavior, vocalization, geographic distribution, and species interactions. By focusing on closed-book evaluation, BAGEL measures an LLM's intrinsic animal-related knowledge without requiring external retrieval during inference. It also facilitates fine-grained analysis across source domains, taxonomic groups, and knowledge categories to identify model strengths and systematic failure modes.

Key takeaway

For AI scientists developing or deploying LLMs in biodiversity or ecological applications, BAGEL offers a critical tool to assess and improve domain-specific knowledge. You should use this benchmark to identify precise strengths and systematic failure modes in your models, ensuring greater reliability and accuracy for specialized animal-related tasks. This can guide targeted fine-tuning or architectural improvements.

Key insights

BAGEL benchmarks LLMs' specialized animal knowledge across diverse categories using a closed-book evaluation.

Principles

• Closed-book evaluation measures intrinsic model knowledge.
• Diverse sources improve benchmark comprehensiveness.

Method

BAGEL constructs question-answer pairs from scientific and reference sources like bioRxiv and Wikipedia, covering taxonomy, morphology, habitat, behavior, vocalization, distribution, and species interactions for closed-book LLM evaluation.

In practice

• Evaluate LLMs for biodiversity applications.
• Identify LLM knowledge gaps in specific animal domains.

Topics

• BAGEL Benchmark
• Language Models
• Animal Knowledge
• Closed-Book Evaluation
• Biodiversity Applications

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.