Exploration of Cross-Modal Text Generation Methods in Smart Justice
Bibliographic record
Abstract
With the development of modern science and technology, information technology has brought great changes to many fields. Smart justice has become one of the increasing areas that people are paying more attention to. For example, large and small cases occur every day, and the legal library is continuously updated. Therefore, a large number of documents and evidence collection archives will bring tremendous pressure on the judiciary. The text generation technology can automatically present the results extracted from these redundant legal data and express the results of the analysis in natural language. It facilitates the business for huge amounts of legal data effectively, which relieves the work pressure of the judicial department. However, the text generation algorithms have not been promoted in justice. Therefore, this paper focuses on what benefits text generation can produce in law and how to apply text generation technology in legal field. The survey provides a comprehensive overview on text generation firstly, through summarizing the existing methods, that is, text to text, data to text, and visual to text. Then, we examine the process of the practical application of text generation in law. Furthermore, this paper puts forward the challenges and possible solutions to the judicial text generation, which provides pointers on future work.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.008 | 0.002 |
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".