GAVAGAI: Referential Alignment for Infant-Scale Vision-Language Models via Balanced Optimal Transport
arXiv:2611.00319v1 Announce Type: new Abstract: A child learns which words name which things from roughly 100 million words of input. Vision-language models trained at a comparable scale bind words to referents far less reliably, and we argue that part of the gap is structural: the usual next-token
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Quelle: arXiv cs.CL (arXiv cs.CL). Original öffnen