In-context Learnin g
The model learns only from examples given in prompt without updating weights.
In-link learning (in-context learning) is the ability to instantly comprehend and implement the mold of a new task from a few examples given in only promptn (prompt). To teach a new task to a model in traditional machine learning, usually when you need to retrain the parameters of the model with the data labeled for that task, in the context learning, the model removes what the task is "cooking" in a few input-types placed into prompt, and this mold applies to a new input; in this process the built-in weights of the model do not change at all, the "learning" takes place in a single forward pass.
This ability is considered to be a self-exposed (emergent) feature when the model scale grows sufficiently; while this ability is weak in small models, it is significantly strengthened in models with billion parameters. In-link learning is very valuable in practice because users get into an expensive and time-consuming retraining process for each new task, only showing several examples, allowing users to adapt the model to a new format, üsluba or classification scheme instantly; this is the main reason why few-shot prompting techniques are used so common in today’s major language model applications.
This ability is considered to be a self-exposed (emergent) feature when the model scale grows sufficiently; while this ability is weak in small models, it is significantly strengthened in models with billion parameters. In-link learning is very valuable in practice because users get into an expensive and time-consuming retraining process for each new task, only showing several examples, allowing users to adapt the model to a new format, üsluba or classification scheme instantly; this is the main reason why few-shot prompting techniques are used so common in today’s major language model applications.