Attention, not scale, drives human-AI alignment in multimodal language prediction

arXiv:2308.06035v4 Announce Type: replace-cross Abstract: Humans routinely draw on visual context to predict upcoming words. To what extent current vision-language models produce comparable behaviour is unclear. Here we placed five state-of-the-art pretrained systems side-by-side with 600 human participants in a web-based Visual-World Paradigm. On each of 100 six-second movie clips, models and participants received either text only or synchronised video and text and judged how likely a specified target word was to appear next; human eye movements were tracked throughout. Adding visual context

Source: arXiv cs.CL — read the full report at the original publisher.

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