Apple Researchers Publish ‘Breakthrough’ Paper on Multimodel LLMs
Michael Nuñez, reporting for VentureBeat:
Apple researchers have developed new methods for training large
language models on both text and images, enabling more powerful
and flexible AI systems, in what could be a significant advance
for artificial intelligence and for future Apple products.
The work, described in a research paper titled “MM1: Methods,
Analysis & Insights from Multimodal LLM Pre-training” that
was quietly posted to arxiv.org this week, demonstrates how
carefully combining different types of training data and model
architectures can lead to state-of-the-art performance on a range
of AI benchmarks.
“We demonstrate that for large-scale multimodal pre-training using
a careful mix of image-caption, interleaved image-text, and
text-only data is crucial for achieving state-of-the-art few-shot
results across multiple benchmarks,” the researchers explain. By
training models on a diverse dataset spanning visual and
linguistic information, the MM1 models were able to excel at tasks
like image captioning, visual question answering, and natural
language inference.
Summary thread on Twitter/X from team member Brandon McKinzie, Hacker News thread, and roundup of commentary from Techmeme. The consensus is that this paper is remarkably open with technical details.
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Michael Nuñez, reporting for VentureBeat:
Apple researchers have developed new methods for training large
language models on both text and images, enabling more powerful
and flexible AI systems, in what could be a significant advance
for artificial intelligence and for future Apple products.
The work, described in a research paper titled “MM1: Methods,
Analysis & Insights from Multimodal LLM Pre-training” that
was quietly posted to arxiv.org this week, demonstrates how
carefully combining different types of training data and model
architectures can lead to state-of-the-art performance on a range
of AI benchmarks.
“We demonstrate that for large-scale multimodal pre-training using
a careful mix of image-caption, interleaved image-text, and
text-only data is crucial for achieving state-of-the-art few-shot
results across multiple benchmarks,” the researchers explain. By
training models on a diverse dataset spanning visual and
linguistic information, the MM1 models were able to excel at tasks
like image captioning, visual question answering, and natural
language inference.
Summary thread on Twitter/X from team member Brandon McKinzie, Hacker News thread, and roundup of commentary from Techmeme. The consensus is that this paper is remarkably open with technical details.