The study explores the influence of multimodal input on language model efficiency. Despite efforts to prevent catastrophic forgetting, results show no consistent advantage of vision on language performance. The research highlights the need for better techniques in multimodal training to bridge the data efficiency gap between models and humans.
The study compares different text and vision input configurations in pretraining large multimodal language models. Results indicate that while vision may marginally enhance grammar-oriented tasks at smaller data scales, it does not consistently improve overall language performance.
The authors conduct experiments using FLAVA architecture with multitask training objectives and WiT dataset sourced from Wikipedia. They evaluate models based on benchmarks for grammar, understanding, and generalization tasks.
Results show that pseudo-perplexity decreases with larger text volumes but worsens as image data increases. Grammaticality evaluations reveal mixed results with no clear advantage of vision across different tasks.
Fine-tuning evaluations on downstream tasks like GLUE/SuperGLUE and MSGS show modest improvements at higher text data scales but no reliable benefits from adding vision.
Cross-situational learning assessments suggest that visual cues do not significantly aid linguistic knowledge acquisition in the models tested. The study acknowledges limitations in computational resources and calls for further research to explore the impact of architectural differences on model performance.
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by Theodor Amar... at arxiv.org 02-29-2024
https://arxiv.org/pdf/2402.17936.pdfDeeper Inquiries