FinRAG aims to enhance retrieval-augmented generation (RAG) methods for financial data analysis, which often struggle with high-level queries using extensive low-level data.As large language models (LLMs) become more widely adopted, the challenges that come with them grow. One of the most pressing issues is hallucination—when an LLM generates plausible but incorrect or misleading information due to gaps in its training data. This situation occurs because LLMs are stochastic text generators, predicting words based on statistical probabilities rather than true understanding. Since their knowledge is static and frozen at the time of training, they struggle with new, proprietary, or real-time information. To address this problem, researchers have explored several techniques, but one—the focus of this installment—stands out as a practical and effective solution: retrieval-augmented generation (RAG).
Rikita Gohil (Sun,) studied this question.