Chain-of-thought text classification

Overview

Chain-of-thought text classification is similar to zero-shot classification since it does not require any labeled data beforehand. The only difference is that, in addition to the label itself, the model generates some additional reasoning behind its choice. In some cases, such an approach might lead to much better performance, but at the cost of higher token consumption.

Example using GPT-4o:

from skllm.models.gpt.classification.zero_shot import CoTGPTClassifier
from skllm.datasets import get_classification_dataset

# demo sentiment analysis dataset
# labels: positive, negative, neutral
X, y = get_classification_dataset()

clf = CoTGPTClassifier(model="gpt-4o")
clf.fit(X,y)
predictions = clf.predict(X)
labels, reasoning = predictions[:, 0], predictions[:, 1]

API Reference

The following API reference only lists the parameters needed for the initialization of the estimator. The remaining methods follow the syntax of a scikit-learn classifier.

CoTGPTClassifier

from skllm.models.gpt.classification.zero_shot import CoTGPTClassifier