From Legal Contracts to Formal Specifications: A Systematic Literature Review
Bibliographic record
Abstract
Abstract The opportunity to automate and monitor the execution of legal contracts is gaining increasing interest in Business and Academia, thanks to the advent of smart contracts, blockchain technologies, and the Internet of Things. A critical issue in developing smart contract systems is the formalization of legal contracts, which are traditionally expressed in natural language with all the pitfalls that this entails. This paper presents a systematic literature review of papers for the main steps related to the transformation of a legal contract expressed in natural language into a formal specification. Key research studies have been identified, classified, and analyzed according to a four-step transformation process: (a) structural and semantic annotation to identify legal concepts in text, (b) identification of relationships among concepts, (c) contract domain modeling, and (d) generation of a formal specification. Each one of these steps poses serious research challenges that have been the subject of research for decades. The systematic review offers an overview of the most relevant research efforts undertaken to address each step and identifies promising approaches, best practices, and existing gaps in the literature.
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 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 | 0.022 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".