VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images

M. Maruf, Arka Daw, Kazi Sajeed Mehrab, Harish Babu Manogaran, Abhilash Neog, Medha Sawhney, Mridul Khurana, James Balhoff, Yasin Bakis, Bahadir Altintas, Matthew J Thompson, Elizabeth G Campolongo, Josef Uyeda, Hilmar Lapp, Henry Bart, Paula Mabee, Yu Su, Wei-Lun Chao, Charles Stewart, Tanya Berger-Wolf et al. (2 additional authors not shown)

Keywords: Organismal biology, Trait Discovery, Task-specific Dataset, Taxonomic classification, Trait Identification, Grounding and Referring, Reasoning for Vision-Language Models

Abstract: Images are increasingly becoming the currency for documenting biodiversity on the planet, providing novel opportunities for accelerating scientific discoveries in the field of organismal biology, especially with the advent of large vision-language models (VLMs). We ask if pre-trained VLMs can aid scientists in answering a range of biologically relevant questions without any additional fine-tuning. In this paper, we evaluate the effectiveness of $12$ state-of-the-art (SOTA) VLMs in the field of organismal biology using a novel dataset, VLM4Bio, consisting of $469K$ question-answer pairs involving $30K$ images from three groups of organisms: fishes, birds, and butterflies, covering five biologically relevant tasks. We also explore the effects of applying prompting techniques and tests for reasoning hallucination on the performance of VLMs, shedding new light on the capabilities of current SOTA VLMs in answering biologically relevant questions using images.

Submission Number: 1392