The field of multimodal large language models (MLLMs) is rapidly evolving, with a focus on improving their ability to understand and interact with real-world data. Recent developments have highlighted the need for more comprehensive benchmarks to evaluate the performance of MLLMs in various scenarios, including mathematical reasoning, object counting, and knowledge editing. Researchers are exploring new approaches to address the limitations of current MLLMs, such as the use of real-world images, multimodal representation, and knowledge association. The integration of language-centered perspectives and cognitive architectures is also being investigated to enhance the interpretability and decision-making capabilities of MLLMs. Noteworthy papers in this area include MathReal, which introduces a benchmark for evaluating math reasoning in MLLMs, and CountQA, which proposes a new benchmark for object counting. Additionally, papers like MultiMedEdit and MDK12-Bench are contributing to the development of more comprehensive evaluation frameworks for MLLMs in medical and educational contexts. RSVLM-QA and ChatENV are also notable for their contributions to remote sensing and environmental monitoring applications.
Advancements in Multimodal Large Language Models
Sources
MathReal: We Keep It Real! A Real Scene Benchmark for Evaluating Math Reasoning in Multimodal Large Language Models
AGI for the Earth, the path, possibilities and how to evaluate intelligence of models that work with Earth Observation Data?
CountQA: How Well Do MLLMs Count in the Wild?
MultiMedEdit: A Scenario-Aware Benchmark for Evaluating Knowledge Editing in Medical VQA
MDK12-Bench: A Comprehensive Evaluation of Multimodal Large Language Models on Multidisciplinary Exams
Remote Sensing Image Intelligent Interpretation with the Language-Centered Perspective: Principles, Methods and Challenges
Surgical Knowledge Rewrite in Compact LLMs: An 'Unlearn-then-Learn' Strategy with ($IA^3$) for Localized Factual Modulation and Catastrophic Forgetting Mitigation
RSVLM-QA: A Benchmark Dataset for Remote Sensing Vision Language Model-based Question Answering
A Dual-Axis Taxonomy of Knowledge Editing for LLMs: From Mechanisms to Functions
ChatENV: An Interactive Vision-Language Model for Sensor-Guided Environmental Monitoring and Scenario Simulation