Unverified60% confidenceFactExact time
The pretraining and fine-tuning paradigm originated from NLP and computer vision, with its core being large-scale data pretraining to learn general low-level features, and downstream tasks adapted using a small amount of labeled data.
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60%
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Long-term
Relevance
8/31/2026
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VerifiedA pre-trained model refers to a model that has been trained in advance on large-scale datasets, which users can directly fine-tune or call to complete specific tasks.80% similarUnverifiedThe pattern of 'pre-training + lightweight downstream head' is becoming a shared practice across fields including NLP, computer vision, and Earth observation.78% similarUnverifiedMultimodal pre-trained models represented by the LayoutLM series jointly model visual, textual, and positional information, advancing the capability frontier in the field of document understanding76% similarUnverifiedDomain fine-tuning refers to supervised fine-tuning or preference optimization of a general pretrained base model using high-quality data from a specific domain, requiring less data and enabling faster iteration compared to training from scratch.76% similarUnverifiedWhen fine-tuning a visual large model for downstream tasks, one can choose to fine-tune only the backend LLM, only the frontend Encoder, both parts, or specify particular layers and modules to train.76% similar
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