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In "auto-CoT", [46] a library of questions are converted to vectors by a model such as BERT. The question vectors are clustered. Questions nearest to the centroids of each cluster are selected. An LLM does zero-shot CoT on each question. The resulting CoT examples are added to the dataset. When prompted with a new question, CoT examples to the ...
The name is a play on words based on the earlier concept of one-shot learning, in which classification can be learned from only one, or a few, examples. Zero-shot methods generally work by associating observed and non-observed classes through some form of auxiliary information, which encodes observable distinguishing properties of objects. [1]
The goal of response prompting is to transfer stimulus control from the prompt to the desired discriminative stimulus. [1] Several response prompting procedures are commonly used in special education research: (a) system of least prompts, (b) most to least prompting, (c) progressive and constant time delay, and (d) simultaneous prompting.
An image of Jenny Joseph modeling for a reference photo used by artist Michael Deas as the basis for the Columbia Pictures logo, shot in the New Orleans apartment of photographer Kathy Anderson ...
Few-shot learning and one-shot learning may refer to: Few-shot learning, a form of prompt engineering in generative AI; One-shot learning (computer vision)
Video modeling is a form of video-based intervention (VBI); other forms include video prompting, computer-based video instruction, and video priming. Several dimensions of effectiveness have been identified for VBI, but important questions regarding VBI remain largely unanswered, both practically and theoretically. [3]
The mild initial curiosity stirred by Zoom-shot movies died quickly, because so few of them were watchable, and because filmmakers quickly found workarounds to create more fluid entertainments ...
One-shot learning is an object categorization problem, found mostly in computer vision. Whereas most machine learning -based object categorization algorithms require training on hundreds or thousands of examples, one-shot learning aims to classify objects from one, or only a few, examples.