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The paper: https://molmo.allenai.org/paper.pdf https://molmo.allenai.org/paper.pdf > Our key innovation is a simple but effective data collection strategy that
by espadrine 2y ago
The paper: https://molmo.allenai.org/paper.pdf https://molmo.allenai.org/paper.pdf
> Our key innovation is a simple but effective data collection strategy that avoids these problems: we ask annotators to describe images in speech
I see this as another example that datasets trump architecture nowadays.
The architecture is not where the innovation is: it is only CLIP embeddings converted to the LLM tokens through MLP with some pooling to reduce the token count.
- imjonse 2y agoArchitectural innovations can definitely help with training/inference speed - that was the case with convolutional networks too - but for model performance (as in 'intelligence' not speed) dataset size and quality were always more important. Even with classical ML models the advice was to stop tweaking the model and clean the data/gather more data first.