BAGEL: Benchmarking Animal Knowledge Expertise in Language Models
Summary: arXiv:2604.16241v1 Announce Type: cross
In recent years, large language models have demonstrated remarkable capabilities across various domains, particularly in broad-domain knowledge and reasoning tasks. However, a significant gap remains in understanding how effectively these models perform when it comes to specialized knowledge, particularly regarding animals. This article introduces BAGEL, a new benchmark designed specifically to evaluate animal knowledge expertise within language models.
Introduction to BAGEL
BAGEL, which stands for Benchmarking Animal Knowledge Expertise in Language models, is meticulously constructed from a variety of scientific and reference sources. These sources include:
- bioRxiv
- Global Biotic Interactions
- Xeno-canto
- Wikipedia
The benchmark utilizes a combination of curated examples and automatically generated closed-book question-answer pairs, ensuring a comprehensive evaluation of animal-related knowledge.
Key Features of BAGEL
BAGEL covers a wide array of topics pertaining to animal knowledge, which can be categorized into several key areas:
- Taxonomy: Understanding the classification of different animal species.
- Morphology: Knowledge of the physical form and structure of animals.
- Habitat: Insights into the natural environments in which various species thrive.
- Behavior: Information on the actions and reactions of animals.
- Vocalization: Knowledge of animal sounds and communication methods.
- Geographic Distribution: Information on where different species are found around the globe.
- Species Interactions: Insights into how different species interact with one another.
Closed-Book Evaluation Approach
One of the standout features of BAGEL is its focus on closed-book evaluation. This approach allows for the assessment of language models’ animal-related knowledge without relying on external retrieval mechanisms during inference. By doing so, BAGEL provides a more accurate measurement of a model’s inherent knowledge and reasoning capabilities.
Fine-Grained Analysis
BAGEL also supports fine-grained analysis across different dimensions, including:
- Source domains – examining the reliability of information from different sources.
- Taxonomic groups – assessing knowledge across various classifications of animals.
- Knowledge categories – identifying strengths and weaknesses in specific areas of animal knowledge.
This level of detailed analysis allows researchers to better understand model performance and identify systematic failure modes, which can guide future improvements in language model training and evaluation.
Conclusion
Overall, BAGEL represents a significant advancement in the evaluation of domain-specific knowledge generalization in language models. By focusing on animal knowledge expertise, this benchmark not only facilitates research in artificial intelligence but also enhances the reliability of language models in biodiversity-related applications. As the field of AI continues to evolve, tools like BAGEL will be crucial in ensuring that language models can effectively contribute to our understanding of the natural world.