An evaluation pipeline quantitatively tests a model across many examples: load a pretrained model, generate outputs, and score them against a dataset or reference answers. section 3.0
A practical pipeline proceeds through these stages:
- Load the pretrained model and evaluation data. section 3.0
- Generate an output for each problem. section 3.0
- Extract the final answer reliably from each response. section 3.0
- Verify correctness by comparing the output with the reference solution, using a symbolic math solver when appropriate. section 3.0
- Aggregate scores to distinguish convincing responses from correct solutions. section 3.0
Math-problem evaluation
Run a pretrained model over a set of math problems, extract each final answer, and compare it with the reference solution through a calculator-like symbolic verifier. The resulting scores measure accurate problem solving rather than persuasive wording. section 3.0
