This article introduces Scikit-LLM, a Python library that integrates large language models like OpenAI's GPT with the Scikit-learn framework to simplify text analysis tasks. It explains and demonstrates two primary classification methods: zero-shot classification, which assigns labels based solely on the model's general knowledge without prior examples, and few-shot classification, which uses a small set of labeled examples within the prompt to improve accuracy. By following a Scikit-learn-style workflow using fit() and predict() methods, users can easily implement these advanced NLP techniques for tasks such as sentiment analysis and topic labeling.
First, using the demonstrations significantly outperforms the no demonstrations method
even with small k (k = 4), and performance drop
from using gold labels to using random labels is
consistently small across varying k, in the range of
0.8–1.6%.7
Interestingly, model performance does
not increase much as k increases when k ≥ 8, both
with gold labels and with random labels.
even with small k (k = 4), and performance drop
from using gold labels to using random labels is
consistently small across varying k, in the range of
0.8–1.6%.7
Interestingly, model performance does
not increase much as k increases when k ≥ 8, both
with gold labels and with random labels.
Following the findings from Min et al. (2022)(opens in a new tab), here are a few more tips about demonstrations/exemplars when doing few-shot:
training GPT to query with few-shot prompting.
