Application of LSTM-Based Seq2Seq Models in Natural Language to SQL Conversion in Financial Domain
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Bibliographic record
Abstract
As a crucial branch of artificial intelligence, Natural Language Processing (NLP) enables computers to understand, interpret, and generate human language, significantly enhancing the efficiency of information retrieval and search. Given the growing demand for data processing in the financial sector, this paper proposes and implements a Seq2Seq model based on the LSTM algorithm to convert natural language queries into SQL statements (NL2SQL) for application in finance. The model demonstrates stable and significant performance improvements over 10 training epochs, with accuracy increasing from 0.75 to 0.9877 and the loss value decreasing from 1.5 to 0.4978. These results validate the accuracy and effectiveness of the proposed LSTM-based Seq2Seq model in handling NLP tasks within the financial domain.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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