A naukri.com initiative
Towards Data Science
4w
68
Retrieval Augmented Classification: Improving Text Classification with External Knowledge
- Text Classification is crucial for various NLP applications, including spam filtering and chatbot categorization.
- Utilizing Large Language Models (LLMs) like zero-shot classifiers can expedite text classification deployment.
- LLMs excel in low-data scenarios and multi-language tasks but require prompt tuning for optimal performance.
- Custom ML models offer flexibility and accuracy in high data regimes but demand retraining and substantial labeled data.
- Retrieval Augmented Generation (RAG) and Few-shot prompting aim to combine LLMs' benefits with custom models' precision.
- RAG incorporates external knowledge, enhancing LLM responses and reducing inaccuracies.
- The method involves curating a knowledge base, finding K-nearest neighbors for input texts, and employing an augmented classifier.
- The combined approach offers dynamic classification with improved accuracy but may incur higher latency and lower throughput.
- Evaluation against a KNN classifier shows enhanced accuracy (+9%) but with trade-offs in speed and performance.
- The method is valuable for agile deployments and situations with limited labeled data, offering quick setup and dynamic adjustments.
Read Full Article
4 Likes
For uninterrupted reading, download the app