In-Context Text Classification System
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An In-Context Text Classification System is an ICL system that implements an ICL text classification algorithm to solve ICL text classification tasks (by leveraging in-context examples provided in the prompt without further fine-tuning of the model, enabling it to classify text based on contextual cues).
- Context:
- It can range from being a Binary In-Context Text Classification System to being a Multiclass In-Context Text Classification System, depending on the number of categories.
- It can range from being a Few-Shot In-Context Text Classification System to a Zero-Shot In-Context Text Classification System, depending on whether examples are provided within the prompt.
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- Example(s):
- an In-Context Sentiment Classification System, that categorizes customer reviews as positive, neutral, or negative based on example reviews in the prompt.
- an In-Context Topic Classification System, that categorizes news articles by topic, such as politics, technology, or sports, using topic-labeled examples.
- an In-Context Spam Detection System, that classifies email messages as spam or not spam based on labeled spam examples in the prompt.
- an In-Context Intent Classification System, that classifies user queries by intent (e.g., buying intent, information-seeking, or complaint) in a customer service setting.
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- Counter-Example(s):
- a Rule-Based Text Classification System, which relies on pre-defined rules rather than adapting to contextual examples in the prompt.
- a Fine-Tuned Text Classification Model, which requires a separate training phase rather than in-context learning.
- a ICL Image Classification System, which assigns multiple labels to each text item rather than a single category.
- See: Text Classification, In-Context Learning System, Prompt Engineering, Large Language Model