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Can LVLMs Uncover the Truth Behind Visual Illusions? An Analysis of Perceptual and Reasoning Capabilities
Liangjie Zhao, Jiaqing Lyu, Kexin Tang +5
Large Vision Language Models have integrated reasoning capabilities, elevating cognitive performance to new levels. However, existing evaluations either focus solely on perception…
Explaining and Tuning Transformer-based LLMs in Arithmetic Tasks with Human Strategies
Luyu Qiu, Jianing Li, Hwanhee Kim +4
Transformer-based large language models (LLMs) continue to achieve state-of-the-art performance across various natural language processing tasks. However, their subpar performance…
Dual Tuning for Reasoning Efficacy-Driven Data Curation in Multimodal LLM Training
Ruobing Zheng, Tianqi Li, Jianing Li +3
Reasoning post-training improves Large Language Models (LLMs) on complex tasks such as mathematics and coding, but its benefits across diverse multimodal tasks remains uncertain. T…
Looping Back to Move Forward: Recursive Transformers for Efficient and Flexible Large Multimodal Models
Ruihan Xu, Yuting Gao, Lan Wang +5
Large Multimodal Models (LMMs) have achieved remarkable success in vision-language tasks, yet their vast parameter counts are often underutilized during both training and inference…