Discover the truth about how language models handle input data and labeling. Get a clear, detailed answer to this common question on the Infosys Certified Applied Generative AI Professional Exam.
Table of Contents
Question
A language model usually creates labels automatically from the inputs
A. True
B. False
C. Depends on the data
Answer
B. False
Explanation
Language models do not usually create labels automatically from the inputs. A language model’s primary function is to learn patterns and relationships within the input text data it is trained on, allowing it to generate new text that is similar in style and content to the training data. However, the model itself does not inherently assign labels or classifications to the input data.
In most cases, if labels are required for a specific task (such as sentiment analysis or named entity recognition), they need to be provided externally as part of the training data. This is typically done through a process called data annotation, where human annotators manually assign labels to the input examples.
There are some advanced techniques, such as zero-shot learning or few-shot learning, where language models can perform certain tasks without explicit training on labeled data for that specific task. However, even in these cases, the models rely on patterns learned from their pretraining on large amounts of unlabeled data, rather than automatically creating labels from the inputs.
In summary, while language models are powerful tools for processing and generating text, they do not automatically create labels for the input data without being explicitly trained to do so using labeled examples.
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