maud_liability_standard_for_no-shop_breach_by_target_non-d&o_representatives
- Task Description: The model must select the answer that best characterizes the liability standard for no-shop breach by Target Non-D&O Representatives in a merger agreement.
- Task Type: Binary classification
- Document Type: merger agreement
- Number of Samples: 157
- Input Length Range: 35-248 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 | gpt-4o-mini | 0.603 | 0.603 | 0.583 | 0.603 | 1.000 | 2025-07-02 | View |
| 2 | meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo | 0.545 | 0.545 | 0.507 | 0.545 | 1.000 | 2025-08-03 | View |
| 3 | claude-3-5-haiku-20241022 | 0.513 | 0.513 | 0.466 | 0.513 | 1.000 | 2025-08-02 | View |
| 4 | meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo | 0.500 | 0.500 | 0.481 | 0.500 | 1.000 | 2025-07-25 | View |
| 5 | gpt-4.1-nano | 0.500 | 0.500 | 0.413 | 0.500 | 1.000 | 2025-07-03 | View |
| 6 | google/gemma-2-27b-it | 0.494 | 0.494 | 0.370 | 0.494 | 1.000 | 2025-07-24 | View |
| 7 | claude-3-haiku-20240307 | 0.487 | 0.487 | 0.477 | 0.487 | 1.000 | 2025-07-28 | View |