The 3rd International Workshop on Mining and Learning in the Legal Domain
Bibliographic record
Abstract
The increasing accessibility of legal corpora and databases create opportunities to develop data-driven techniques and advanced tools that can facilitate a variety of tasks in the legal domain, such as legal search and research, legal document review and summary, legal contract drafting, and legal outcome prediction. Compared with other application domains, the legal domain is characterized by the huge scale of natural language text data, the high complexity of specialist knowledge, and the critical importance of ethical considerations. The MLLD workshop aims to bring together researchers and practitioners to share the latest research findings and innovative approaches in employing data mining, machine learning, information retrieval, and knowledge management techniques to transform the legal sector. Building upon the previous successes, the third edition of the MLLD workshop will emphasize the exploration of new research opportunities brought about by recent rapid advances in Large Language Models and Generative AI. We encourage submissions that intersect computer science and law, from both academia and industry, embodying the interdisciplinary spirit of CIKM.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 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".