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Keyword: data-efficient training ×Clear all

1 claim from 1 paper

1. Computer Science › Machine Learning and Algorithms

   LLMaAA: Making Large Language Models as Active Annotators

   Zhang, Li, Ma, Zhou and Zou · 2023

   The paper proposes LLMaAA, which uses an LLM as an annotator inside an active learning loop, and tests it on named entity recognition and relation extraction.

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   1. UncheckedWith LLMaAA, small task-specific models trained on LLM-made labels can beat the LLM itself using only hundreds of human-annotated examples.“With LLMaAA, task-specific models trained from LLM-generated labels can outperform the teacher within only hundreds of annotated examples, which is much more cost-effective than other baselines.”