Omkar2118/datasets huggingface dataset:
MathEquiv
Full name: mathematical statement equivalence

MathEquiv dataset is accompanied to [EquivPruner](https://github.com/Lolo1222/EquivPruner) . It is specifically designed for **mathematical statement equivalence** , serving as a versatile resource applicable to a variety of mathematical tasks and scenarios. It consists of almost 100k math sentences pair with equivalence result and reasoning step generated by GPT-4O.

The dataset consists of three splits:

- `train` with 77.6k problems for training.
- `test` with 9.83k samples for testing.
- `valid` with 9.75k samples for validation.

We implemented a five-tiered classification system. This granular approach was adopted to enhance the stability of the GPT model's outputs, as preliminary experiments with binary classification (equivalent/non-equivalent) revealed inconsistencies in judgments. The five-tiered system yielded significantly more consistent and reliable assessments:

- Level 4 (Exactly Equivalent): The statements are mathematically interchangeable in all respects, exhibiting identical meaning and form.
- Level 3 (Likely Equivalent): Minor syntactic differences may be present, but the core mathematical content and logic align.
- Level 2 (Indeterminable): Insufficient information is available to make a definitive judgment regarding equivalence.
- Level 1 (Unlikely Equivalent): While some partial agreement may exist, critical discrepancies in logic, definition, or mathematical structure are observed.
- Level 0 (Not Equivalent): The statements are fundamentally distinct in their mathematical meaning, derivation, or resultant outcomes.

Introducing paper: EquivPruner: Boosting Efficiency and Quality in LLM-Based Search via Action Pruning