Text2Graph: A Method for Automatically Drawing Statistical Graphs Based on Large Language Models
Bibliographic record
Abstract
With the in-depth development of digitalization and informatization, data visualization technology is undergoing unprecedented changes. As an important tool of data visualization, statistical graphs have been widely used in various fields. However, the traditional statistical graph drawing process is heavily dependent on the operation skills of professionals, and there are some problems such as cumbersome process and low efficiency. Therefore, an automatic drawing method of statistical graphs based on large language model is innovatively proposed and named Text2Graph. Via deeply integrating large language model technology to carry out end-to-end automatic generation from natural language instructions to visual statistical graphics. Text2Graph covers three core modules: intelligently identifying users' drawing requirements, automatically generating and assembling drawing codes, and automatically executing drawing scripts. The experimental results show that Text2Graph method performs well in eight conventional statistical graph automatic drawing tasks under specific compilation environment, and the qualified rate and excellent rate of automatic drawing reach 100% and 80% respectively. It can greatly enhance the intelligent level and efficiency of statistical graph drawing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".