CanAL: a Canadian Appeal Law Legal Language Model for LegalJudgment Prediction
Bibliographic record
Abstract
Law is one of the knowledge domains that are most reliant on textual material. In this age of legal big data, and with the increased availability of legal text online, many researchers started working on the development of legal intelligent systems and applications. Over the last few years, researchers have focused on predicting judicial case outcomes using Natural Language Processing (NLP) and Machine Learning (ML) methods over case documents. Thus, Legal Judgment Prediction (LJP) is the task of automatically predicting the outcome of a court case given the case description. The NLP application to legal judgments, that our proposed methodology focuses on, is to predict the outcomes of cases by looking only at the text of cases. Because appeal court decisions are often binary, as in ’Allow’ or ’Dismiss’, the task is defined as a binary classification problem. This is the general approach in the literature as well. However, many of the previous LJP approaches utilized traditional classifiers or standard general language models (LMs). In this paper, we further pre-trained the LegalBERT model on our collected corpus that combines around 50,000 documents of Canadian case law and legislation which resulted in (CanAL), a Canadian Appeal-Law BERT-based legal LM. In addition, we constructed and labeled a Canadian Appeal-Law dataset that contains a collection of decisions from different higher courts in Canada. Moreover, we proposed a novel Ensemble-Hierarchical CanAL (EH-CanAL) architecture that simulates the actual voting setting in appellate courts showing great promise in LJP performance within Canadian case law. The results of our study demonstrate the potential of the proposed approaches to reshape traditional judicial decision making and the efficacy of domain-specific LMs. Through this study, we hope to establish the basis for future research on the appellate law system of Canada and to offer a baseline for future work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".