ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios

Understand

Active learning is designed to minimize annotation efforts by prioritizing instances that most enhance learning.

• However, many active learning strategies struggle with a `cold-start' problem, needing substantial initial data to be effective.
• This limitation reduces their utility in the increasingly relevant few-shot scenarios, where the instance selection has a substantial impact.
• To address this, we introduce ActiveLLM, a novel active learning approach that leverages Large Language Models such as GPT-4, o1, Llama 3, or Mistral Large for selecting instances.