Retrieval-augmented generation system for stock market information retrieval
Original title: Enhancing Stock Market Information Retrieval with Retrieval-Augmented Generation
A RAG system for stock market queries achieves high ROUGE scores and 1.16 second average response time on 100 queries.
Traditional NLP systems struggle with contextual accuracy in dynamic financial environments. The RAG system integrates retrieval-based methods with generative capabilities to produce accurate financial responses. Evaluation on a dataset of 100 stock market-related queries uses ROUGE-1, ROUGE-2, and ROUGE-L metrics.
- High ROUGE scores indicate strong alignment with reference documents
- Average response time of 1.16 seconds
- Capable of providing accurate and timely stock market insights
From the original article
1 min read at huggingface.co
Natural Language Processing (NLP) has been widely applied in financial domains, enabling automated analysis and retrieval of stock market information. However, traditional NLP-based systems often struggle with maintaining contextual accuracy and generating relevant responses in dynamic financial environments.
By Huan Phong Thai, Thu Anh Ngoc Le, Nhu Huynh Thi Le · huggingface.co
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