Can foundation models label data like humans?
Read the original on Hugging Face Blog →The Flow has not summarised this story yet — read it at Hugging Face Blog.
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arXiv:2606. 05107v1 Announce Type: cross Abstract: We propose a label-free approach to adapt powerful but generic vision foundation models to specialized scientific domains.
arXiv:2610.08314v1 Announce Type: new Abstract: Linear regression and kernel smoothing offer tractable explanations of in-context learning: in both, the features determine the weight assigned to each...
arXiv:2403. 07008v3 Announce Type: replace-cross Abstract: The evaluation of machine learning models using human-labeled validation data can be expensive and time-consuming.
arXiv:2607. 17653v1 Announce Type: cross Abstract: Source-free universal domain adaptation (SF-UniDA) adapts a pre-trained source model to an unlabeled target domain under both covariate and label shifts, without access to source data.
arXiv:2608. 07705v1 Announce Type: new Abstract: Clinical foundation models trained on large-scale patient data are increasingly used for decision support, screening, and public health.
The paper surveys how Large Foundation Models (LFMs) can be integrated into Human‑AI Collaboration (HAI) to enhance problem‑solving and decision‑making. It outlines four key areas—human‑guided model development, collaborative design principles, ethical and governance frameworks, and high‑stakes applications—while emphasizing that effective HAI systems arise from careful, human‑centered design rather than merely stronger models. The survey also identifies open challenges related to safety, fairness, and control, aiming to guide future research toward reliable, trustworthy, and beneficial LFM‑based partnerships.