LLMs enable document classification without extensive labeled data

This article details how to build a document classification system using large language models, particularly when labeled training data is scarce. It suggests using LLMs for zero-shot or few-shot classification by providing category descriptions and optional examples, contrasting this with fine-tuning BERT-style models which requires extensive labeled data and lower latency. The guide emphasizes forcing structured JSON output from the LLM and demonstrates how to implement batch processing using asynchronous programming to improve throughput. AI

IMPACT Provides a practical framework for leveraging LLMs in document processing workflows, reducing the need for extensive labeled data.

RANK_REASON Article describes a practical application and implementation of existing LLM technology for a specific task, rather than a new release or research.

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