Sparse Attention Combined with RAG Technology for Financial Data Analysis
Why this work is in the frame
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Bibliographic record
Abstract
In response to the challenges of multimodal data integration, real-time information retrieval, model hallucination, and lack of interpretability in financial stock analysis, this paper proposes an innovative financial analysis framework—FSframe. It aims to address multiple challenges in stock analysis within the financial sector. The framework integrates various technological modules to provide comprehensive and efficient solutions for stock trend prediction and financial question answering tasks. First, FSframe optimizes large language models (LLMs), enhancing their adaptability to financial tasks, and incorporates prompt engineering to mitigate potential hallucination issues during the generation process, thereby improving the accuracy and reliability of the analysis. Secondly, the framework introduces Retrieval-Augmented Generation (RAG) technology, creating a dynamically updated financial knowledge base that enables the model to retrieve and integrate the latest market data, providing real-time external knowledge support for tasks. Furthermore, FSframe adopts a sparse attention mechanism, optimizing the processing efficiency of time-series data by filtering irrelevant information and focusing on key points, while also achieving efficient integration of time-series and textual data. Finally, through its modular design, FSframe organically combines the aforementioned advanced technologies, forming an innovative solution that blends multimodal data processing with real-time analysis, offering strong technical support for intelligent analysis in the financial sector. Validation on large-scale financial datasets (including historical stock prices, financial news, and market announcements) shows that FSframe significantly improves prediction accuracy and real-time responsiveness in stock trend forecasting and financial question answering tasks. Experimental results indicate that FSframe offers significant advantages in multimodal data integration, real-time performance, and interpretability, demonstrating excellent task adaptability and addressing the shortcomings of traditional methods. The FSframe framework not only provides an innovative solution for stock analysis in the financial sector but also opens new pathways for the development of intelligent financial technologies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it