The paper explores the concept of using large language models (LLMs) for classification tasks by treating them as feature engineers. It suggests that LLMs can extract meaningful features from data that are useful for classification purposes.
The chart will appear after repeat observations. The current metric comes from the source.
29 source pointsReal observations only. History before source connection is not reconstructed.
This approach could help in understanding how LLMs contribute to machine learning tasks beyond just text generation.
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