Analysis of information sources in references of the Wikipedia article "プロンプトエンジニアリング" in Japanese language version.
In prompting, a pre-trained language model is given a prompt (e.g. a natural language instruction) of a task and completes the response without any further training or gradient updates to its parameters... The ability to perform a task via few-shot prompting is emergent when a model has random performance until a certain scale, after which performance increases to well-above random
Thus we show how trained Transformers become mesa-optimizers i.e. learn models by gradient descent in their forward pass
Training a model to perform in-context learning can be viewed as an instance of the more general learning-to-learn or meta-learning paradigm
The directional stimulus serves as hints or cues for each input query to guide LLMs toward the desired output, such as keywords that the desired summary should include for summarization.
Using only 3-5 images of a user-provided concept, like an object or a style, we learn to represent it through new "words" in the embedding space of a frozen text-to-image model.
