maud_change_in_law__subject_to_disproportionate_impact_modifier
- Task Description: The model must determine whether changes in law that have a disproportionate impact qualify for Material Adverse Effect (MAE) based on an excerpt from a merger agreement.
- Task Type: Binary classification
- Document Type: merger agreement
- Number of Samples: 100
- Input Length Range: 623-1744 tokens
- Evaluation Metrics: accuracy (maximize), balanced_accuracy (maximize), f1_macro (maximize), f1_micro (maximize), valid_predictions_ratio (maximize)
- Tags: corporate law, interpretation
- Paper: LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models
- Dataset Download: https://hazyresearch.stanford.edu/legalbench/
7 submissions
| Rank | Model | accuracy | balanced_accuracy | f1_macro | f1_micro | valid_predictions_ratio | Date | Results |
|---|---|---|---|---|---|---|---|---|
| 1 | claude-3-5-haiku-20241022 | 0.879 | 0.500 | 0.468 | 0.879 | 1.000 | 2025-08-02 | View |
| 2 | gpt-4.1-nano | 0.879 | 0.500 | 0.468 | 0.879 | 1.000 | 2025-07-03 | View |
| 3 | claude-3-haiku-20240307 | 0.879 | 0.500 | 0.468 | 0.879 | 1.000 | 2025-07-28 | View |
| 4 | meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo | 0.879 | 0.500 | 0.468 | 0.879 | 1.000 | 2025-08-03 | View |
| 5 | google/gemma-2-27b-it | 0.879 | 0.500 | 0.468 | 0.879 | 1.000 | 2025-07-24 | View |
| 6 | gpt-4o-mini | 0.869 | 0.494 | 0.465 | 0.869 | 1.000 | 2025-07-02 | View |
| 7 | meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo | 0.859 | 0.489 | 0.462 | 0.859 | 1.000 | 2025-07-25 | View |