The NeurIPS 2024 Preshow: What matters when building vision-language models?

A discussion with Hugging Face researcher Hugo Laurençon ahead of NeurIPS 2024 examines the practical challenges of building vision-language models, which combine image understanding with language generation for tasks such as interpreting documents and generating code from screenshots. The research compares cross-attention and fully autoregressive self-attention architectures, finding that simpler self-attention approaches can be more effective and efficient when vision and language backbones are unfrozen during training. It also explores efficiency methods including learned visual-token pooling and image splitting for high-resolution text and document understanding. Data curation for the Idefics2 model relies on The Cauldron dataset, which combines interleaved image-text data, synthetic-caption image pairs, PDFs, and text-only instruction data, while using filtering practices to address opt-out, NSFW, and other safety concerns. The discussion emphasizes that synthetic data can improve quality but may introduce bias, that red-teaming reveals potential misuse risks, and that interactive demonstrations are essential alongside benchmarks for finding model failures, artifacts, and areas requiring continuous refinement.

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