#text-classification

#text-classification

TnT-LLM framework enhances text classification using LLM for taxonomy generation and training lightweight classifiers with pseudo-labels from the generated taxonomy.

There is a notable disparity in judgment between human annotators and LLMs regarding user query classifications, particularly in complex intent categories.

The LLM-based framework emphasizes robust execution through structured prompts and guardrail tests to ensure reliable output formatting.

The study explores using large language models for efficient text classification through pseudo-labeled datasets.

Reliability in text classification is crucial and can be assessed using multiple annotators and LLMs to align with human consensus.

LLMs handle most classification issues effectively, but surpassing their performance requires advanced algorithms like R.E.D.

Data annotation is vital for machine learning models, enabling them to learn from raw data effectively.

The article introduces a new evaluation suite for taxonomy generation and text classification using a combination of evaluation strategies.