Part of Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Datasets and Benchmarks Track
M. Maruf, Arka Daw, Kazi Sajeed Mehrab, Harish Babu Manogaran, Abhilash Neog, Medha Sawhney, Mridul Khurana, James Balhoff, Yasin Bakis, Bahadir Altintas, Matthew Thompson, Elizabeth Campolongo, Josef Uyeda, Hilmar Lapp, Henry Bart, Paula Mabee, Yu Su, Wei-Lun (Harry) Chao, Charles Stewart, Tanya Berger-Wolf, Wasila Dahdul, Anuj Karpatne
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.