EconVNNewsBot-2: Enhancing Economic News Retrieval with Retrieval-Augmented Thoughts for Improved Question-Answering in Vietnam

The rapid expansion of Vietnamese economic news presents challenges for investors and entrepreneurs seeking accurate, domain-specific information. Traditional search engines struggle with complex queries, revealing a gap in effective information retrieval. In our previous work (Paper ID: 22 in ATAC 2024), we introduced EconVNNewsBot, a specialized Question-Answering (QA) system for Vietnamese economic news, where we used Retrieval-Augmented Generation (RAG) and Chain-of-Thought (CoT) reasoning separately. Although effective, these methods treated retrieval and reasoning as distinct steps, limiting system efficiency and coherence. This paper extends our prior work by employing a more advanced approach: Retrieval-Augmented Thoughts (RAT). RAT unifies retrieval and reasoning into a single integrated framework, providing more precise and context-sensitive responses with improved efficiency. Our new experiments show that the RAT-based approach enhances user engagement, reduces retrieval time, and simplifies the information-gathering process. These findings suggest that RAT can transform how economic news is accessed, benefiting both information seekers and news agencies. The research is supported by the unique EconVNNews dataset, compiled using the EconVNNewsCrawl tool developed by our team, ensuring the system’s robust performance in the economic domain.