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Cascaded Self-Evaluation Augmented Training for Efficient Multimodal Large Language Models

Efficient Multimodal Large Language Models (EMLLMs) have rapidly advanced recently. Incorporating Chain-of-Thought (CoT) reasoning and step-by-step self-evaluation has improved their performance. However, limited parameters often hinder EMLLMs from effectively using self-evaluation during inference. Key challenges include synthesizing evaluation data, determining its quantity, optimizing training and inference strategies, and selecting appropriate prompts. To address these issues, we introduce Self-Evaluation Augmented Training (SEAT). SEAT uses more powerful EMLLMs for CoT reasoning, data selection, and evaluation generation, then trains EMLLMs with the synthesized data. However, handling long prompts and maintaining CoT reasoning quality are problematic. Therefore, we propose Cascaded Self-Evaluation Augmented Training (Cas-SEAT), which breaks down lengthy prompts into shorter, task-specific cascaded prompts and reduces costs for resource-limited settings. During data synthesis, we employ open-source 7B-parameter EMLLMs and annotate a small dataset with short prompts. Experiments demonstrate that Cas-SEAT significantly boosts EMLLMs' self-evaluation abilities, improving performance by 19.68%, 55.57%, and 46.79% on the MathVista, Math-V, and We-Math datasets, respectively. Additionally, our Cas-SEAT Dataset serves as a valuable resource for future research in enhancing EMLLM self-evaluation.

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