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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 first paper on zero-shot learning in natural language processing appeared in a 2008 paper by Chang, Ratinov, Roth, and Srikumar, at the AAAI’08, but the name given to the learning paradigm there was dataless classification. [3] The first paper on zero-shot learning in computer vision appeared at the same conference, under the name zero ...
A question answering task is considered "open book" if the model's prompt includes text from which the expected answer can be derived (for example, the previous question could be adjoined with some text which includes the sentence "The Sharks have advanced to the Stanley Cup finals once, losing to the Pittsburgh Penguins in 2016." [125]).
No response (provide prompt 3) Prompted Incorrect (provide prompt 3) Physical Prompt: Gives Prompt 3: Puts hands on learner's shoulders and physically guides him to sit: Prompted Correct: Sits (provide reinforcer) No response (ignore). This response is unlikely: re-evaluate prompt and/or value of reinforcer: No response (ignore). This response ...
28 Bézier patches (32 with the bottom) [1] Also called the "Newell teapot". One of the first models not to be measured. Cornell box: 1984 Cindy M. Goral, Kenneth E. Torrance, Donald P. Greenberg, Bennett Battaile at Cornell University: Originally meant to be compared to real-life setup to test physicality of simulated optics 5 quads, 1 light ...
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A typical constellation model has P(3 ~ 7) parts, with N(~100) interest regions. Thus a P-dimensional vector h assigns one region of interest (out of N regions) to each model part (for P parts). Thus h denotes a hypothesis (an assignment of interest regions to model parts) for the model and a full constellation model is represented by summing ...
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)