DianJin-Fin-PRM-Data

# DianJin-Fin-PRM Dataset ## Overview DianJin-Fin-PRM is a Chinese financial domain **Process Reward Model (PRM)** training dataset. It contains 4,969 samples of financial exam questions with step-by-step reasoning traces and multi-dimensional quality annotations. ## Dataset Structure | Field | Type | Description | |---|---|---| | `名称` | string | Exam name (e.g., 初级经济师) | | `科目` | string | Subject (e.g., 金融实务) | | `章节` | string | Chapter | | `task` | string | Question type (e.g., 单项选择题) | | `question` | string | Question text | | `choices` | string | Answer choices (JSON dict) | | `answer` | string | Ground truth answer | | `analysis` | string | Reference analysis | | `analysis_length` | int | Length of reference analysis | | `trace` | string | Step-by-step reasoning trace | | `final_answer` | string | Model's final answer with reasoning | | `knowlegde_coverage_response` | string | Knowledge coverage evaluation response | | `knowledge_coverage_score` | float | Knowledge coverage score (0-1) | | `coverage_knowledge_points` | string | Covered knowledge points (JSON list) | | `importance_labels` | string | Step importance labels (JSON list) | | `analysis_knowledge` | string | Knowledge points from analysis (JSON list) | | `knowledge_accuracy` | string | Per-step knowledge accuracy (JSON list) | | `quality_score` | string | Per-step quality scores (JSON list of dicts with logical_soundness, step_correctness, target_progression) | | `model_answer` | string | Model's extracted answer | | `accuracy_score` | string | Per-step accuracy scores (JSON list) | | `step_scores` | string | Aggregated per-step scores (JSON list) | | `step_labels` | string | Per-step binary labels (JSON list) | | `trajectory_label` | int | Overall trajectory label (1=correct, 0=incorrect) | ## Usage ```python from datasets import load_dataset dataset = load_dataset("DianJin/DianJin-Fin-PRM-Data") ``` ## Citation If you use this dataset, please cite: ```bibtex @misc{dianjin-fin-prm, title={DianJin-Fin-PRM: A Chinese Financial Process Reward Model Dataset}, author={DianJin Team}, year={2025} } ``` ## License Apache License 2.0