SpecVQA: A Benchmark for Spectral Understanding and Visual Question Answering in Scientific Images

Problem
This paper addresses the gap in capability for multimodal large language models (MLLMs) in understanding and reasoning about scientific spectral data, which is often unstructured and domain-specific. The authors introduce SpecVQA, a benchmark specifically designed for evaluating MLLMs on spectral understanding and visual question answering (VQA) in scientific images. This work is presented as a preprint and has not yet undergone peer review.

Method
The core technical contribution is the SpecVQA benchmark, which includes 620 figures and 3100 expert-annotated question-answer pairs derived from peer-reviewed literature. The benchmark encompasses seven representative types of spectra, facilitating both direct information extraction and domain-specific reasoning tasks. To address the challenge of high token length while retaining essential curve characteristics, the authors propose a spectral data sampling and interpolation reconstruction approach. This method effectively reduces the input size for MLLMs, enhancing their performance on the benchmark. The authors also conduct ablation studies to validate the efficacy of their proposed method, demonstrating significant performance improvements.

Results
The authors evaluate several prominent MLLMs on the SpecVQA benchmark and present a leaderboard to summarize their findings. While specific performance metrics are not disclosed in the abstract, the ablation studies indicate substantial improvements in model performance due to the proposed spectral data sampling and interpolation techniques. The results suggest that the benchmark effectively challenges existing models and provides a clear metric for assessing their capabilities in spectral understanding.

Limitations
The authors acknowledge that the benchmark is limited to seven types of spectra, which may not encompass the full diversity of scientific spectral data. Additionally, the reliance on expert-annotated question-answer pairs may introduce biases based on the annotators’ interpretations. The paper does not address potential scalability issues related to the benchmark’s size or the computational resources required for training and evaluating MLLMs on this dataset.

Why it matters
The introduction of SpecVQA represents a significant advancement in the evaluation of MLLMs for scientific applications, particularly in the realm of spectral data analysis. By providing a structured benchmark, this work lays the groundwork for future research aimed at enhancing the capabilities of visual-language models in scientific contexts. The implications extend to various domains, including materials science, chemistry, and biology, where spectral data plays a crucial role in data interpretation and analysis. This benchmark could catalyze further developments in multimodal learning, enabling more sophisticated models that can integrate visual and textual information for scientific inquiry.

Authors: Jialu Shen, Han Lyu, Suyang Zhong, Hanzheng Li, Haoyi Tao, Nan Wang, Changhong Chen, Xi Fang
Source: arXiv:2604.28039
URL: https://arxiv.org/abs/2604.28039v1