Contrastive Learning for Legal Judgment Prediction
Bibliographic record
Abstract
Legal judgment prediction (LJP) is a fundamental task of legal artificial intelligence. It aims to automatically predict the judgment results of legal cases. Three typical subtasks are relevant law article prediction, charge prediction, and term-of-penalty prediction. Due to the wide range of potential applications, LJP has attracted a great deal of interest, prompting the development of numerous approaches. These methods mainly focus on building a more accurate representation of a case’s fact description in order to improve the performance of judgment prediction. They overlook, however, the practical judicial scenario in which human judges often compare similar law articles or possible charges before making a final decision. To this end, we propose a supervised contrastive learning framework for the LJP task. Specifically, we train the model to distinguish (1) various law articles within the same chapter of a Law and (2) similar charges of the same law article or related law articles. By this means, the fine-grained differences between similar articles/charges can be captured, which are important for making a judgment. Besides, we optimize our model by identifying cases with the same article/charge labels, allowing it to more effectively model the relationship between the case’s fact description and its associated labels. By jointly learning the LJP task with the aforementioned contrastive learning tasks, our model achieves better performance than the state-of-the-art models on two real-world datasets.
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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 | 0.003 | 0.013 |
| 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 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".