Theoremqa Eval

theoremqa-eval

TheoremQA: A Theorem-driven Question Answering dataset — Chen et al. (2023) (arXiv:2305.12524, 2023)

What this evaluates

Evaluates LLMs' ability to apply domain-specific theorems from mathematics, physics, computer science, and finance to solve complex scientific problems. It probes theorem-driven reasoning, numerical computation, and program generation capabilities.

Datasets

• TheoremQA — total 800; splits: test (800)

Metrics

• accuracy (primary) — range: percent
  • Percentage of correctly answered questions. The model's final output is compared against the ground truth answer, with exact matching or numerical tolerance applied depending on the answer type.

Input / output format

Input: A question requiring theorem-driven reasoning across STEM domains. For multimodal variants, an image is provided and converted to a text caption.

Output: A final answer in the required format (integer, float, boolean, list, or multiple-choice option), or a Python program to compute the answer.

Scoring recipe

def calculate_accuracy(predictions, gold_answers):
    correct = 0
    for pred, gold in zip(predictions, gold_answers):
        if normalize_answer(pred) == normalize_answer(gold):
            correct += 1
    return (correct / len(predictions)) * 100

Common pitfalls

• Models often generate correct logical steps in Chain-of-Thought or Program-of-Thoughts but make minor calculation errors in intermediate steps, leading to incorrect final answers.
• Multimodal questions rely on BLIP-generated captions, which cause significant information loss for diagrammatic inputs, making them nearly impossible for text-only models.
• Simply concatenating theorem definitions as additional prompt context yields negligible accuracy gains (<1%) due to the abstract, symbolic nature of theorems.

Evidence (verbatim from paper)

With CoT prompting, GPT-3 (text-davinci-002) and GPT-3.5 models are only achieving 16.6% and 22.8% accuracy. By adopting the program as the intermediate reasoning form, both models can gain reasonable improvements.

Citation

@misc{chen2023theoremqa,
  title={TheoremQA: A Theorem-driven Question Answering dataset},
  author={Chen et al. (2023)},
  year={2023},
  note={arXiv:2305.12524}
}

• arXiv: 2305.12524