ChiekoSeren/RWKV-Thinking-problem-classify

RWKV-Thinking Problem Difficulty Classification

This model is designed to predict the difficulty of problems within the RWKV-Thinking dataset. This prediction is used to estimate the number of reasoning paths required for multi-path reasoning.

Model Overview:

This model leverages the RWKV-Thinking-problem-classify-v1 dataset to classify the difficulty of problems. The difficulty classification is a crucial step in determining the complexity of reasoning required to solve a problem, which directly influences the number of reasoning paths explored during multi-path reasoning.

Intended Use:

• Predicting the difficulty level of problems in the RWKV-Thinking dataset.
• Estimating the number of reasoning paths needed for multi-path reasoning.
• Evaluating the performance of language models in understanding and classifying problem complexity.
• Supporting research in reasoning, problem-solving, and natural language understanding.

Dataset Details:

• Dataset Name: RWKV-Thinking-problem-classify-v1
• Dataset Description: This dataset assesses the diversity of problem types and the probability of successful problem-solving across various contexts. It includes a range of problem statements, classifications, and associated metadata.
• Dataset Creation:
• Curation Rationale: Created to provide a benchmark for evaluating how well models like RWKV can handle diverse problem types and predict solution success.
• Source Data: Problems may be sourced from synthetic generation, educational materials, or curated problem-solving repositories.
• Preprocessing: Problems were standardized, categorized, and assigned diversity and success probability scores.
• Annotations: Manual annotation by domain experts or automated scoring based on predefined criteria. Annotators assessed problem complexity, uniqueness, and solvability.
• Fine-tuning Dataset Size: 1K < n < 10K

Model Training:

• Model Architecture: BERT
• Training Data: RWKV-Thinking-problem-classify-v1 dataset.

Ethical Considerations:

• Social Impact: This model can advance AI research in reasoning and education, potentially aiding in personalized learning systems or automated tutoring tools.
• Biases: Potential biases may arise from the selection of problem categories or the subjectivity in assigning diversity and success scores. Users should evaluate these factors for their specific use case.
• Limitations: Limited scope to predefined categories. Success probability may vary based on model capability or user expertise.