Datasets:
| name: ContractBench | |
| description: > | |
| ContractBench measures how well an AI agent reviews and redlines a commercial contract the way an in-house attorney | |
| would: deciding where to intervene, what to change, what to leave alone, when to ask the counterparty a question and | |
| when to escalate a point to its own side. Each task is a real Word document with grounding documents and a brief; the | |
| agent works in the .docx with tracked changes and comments and is graded against pass-or-fail rubric items written by | |
| the attorney who reviewed the same contract. 350 tasks from 35 scenarios (210 public, 140 held out), five attorneys per | |
| scenario, two negotiation turns. Scores are the weighted rubric pass rate on completed rollouts, 0 to 100; | |
| leaderboard.md in this repository lists the published results. | |
| evaluation_framework: harbor | |
| tasks: | |
| - id: contractbench_private | |
| config: default | |
| split: test | |
| - id: contractbench_public | |
| config: default | |
| split: test | |
| - id: contractbench_full | |
| config: default | |
| split: test | |