Large Language Models in Legal Systems: A Survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper provides a comprehensive survey of the role of large language models (LLMs) in legal systems. It examines their applications across key areas such as legal document drafting, case analysis, research, compliance monitoring, and education. In addition to mapping these use cases, the survey reviews datasets and benchmarks that enable the training and fine-tuning of LLMs for legal tasks. The analysis highlights both the opportunities and challenges of adopting LLMs in practice, including issues of bias, interpretability, accuracy, and ethical risk. Particular attention is given to the limitations of current models and the risks of overstating their reliability in high-stakes legal contexts. By synthesizing recent advancements, this paper provides a balanced perspective on the current state of LLMs in the legal domain and outlines future directions for research and practice aimed at improving their effectiveness, accountability, and responsible deployment.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it