Results indicate that MultivationBench presents a significant challenge: all tested models struggle to maintain consistent motivation reasoning across sequential contexts, revealing a critical disconnect between static recognition capabilities and the dynamic reasoning essential for human-like social understanding.
SFF-CLIP (Self-annotated Fine-grained Fine-tuning for CLIP), which only uses image-text pairs as input to boost the fine-grained representation ability in the CLIP fine-tuning, while maintaining the global visual-semantic consistency.
Chen-Yang Zhao, Wei Lin, Antoni B. Chan et al.· arXiv.org· 0 citations
The findings reveal that transformer-based large language models exhibit learning patterns similar to those of human learners, with a faster learning speed for simpler subtasks compared to more complex ones, which suggests that transformer-based LLMs may share cognitive processes with human learners in arithmetic.
Luyu Qiu, Jianing Li, Hwanhee Kim et al.· 0 citations
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