Unverified50% confidenceFactExact time
Foundation models refer to models pretrained on large-scale general-purpose corpora that can be adapted to a variety of downstream tasks through fine-tuning or prompting, and are usually categorized by modality into text, image, audio and other types.
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50%
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Long-term
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10/19/2026
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UnverifiedUsing large-scale pre-trained models (such as Whisper and HuBERT) as a foundation for fine-tuning on specific tasks allows small datasets to be reframed as few-shot fine-tuning scenarios.77% similarVerifiedFine-tuning refers to performing secondary training on a foundation model that has completed general-text pre-training, using domain-specific labeled data to adjust the model's parameters76% similarUnverifiedCertain specific phrasings in the original prompt activate shortcuts entrenched during model training, causing the model to apply fixed patterns in its responses; after paraphrasing, the model is forced to perform more general reasoning, thereby improving output quality.76% similarUnverifiedThe inputs and outputs of world foundation models are multimodal physical signals such as video, depth maps, and point clouds, rather than pure text tokens; the training objective is to predict the evolution of a scene over time.76% similar
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