metadata
license: apache-2.0
tags:
- math
- reasoning
- dynamic-analysis
- bode-plot
- llm-evaluation
- exponential-functions
pretty_name: MathBode-ExponentialInterest
size_categories:
- 1K<n<10K
MathBode-ExponentialInterest: Exponential Functions Domain
Compound interest and exponential growth problems from the MathBode benchmark.
This dataset is part of the MathBode benchmark, which evaluates the dynamic reasoning capabilities of large language models (LLMs) by treating parametric math problems as dynamic systems. Instead of testing static accuracy on fixed problems, MathBode sinusoidally varies a parameter and measures the model's response in terms of gain (amplitude tracking) and phase (reasoning lag), analogous to a Bode plot in control theory.
About This Domain
This dataset contains Exponential Functions problems specifically, with approximately 9,408 prompts covering:
- 3 question variants with different constants
- Sinusoidal parameter sweeps at 5 frequencies (1, 2, 4, 8, 16 cycles)
- 3 phase offsets (0°, 120°, 240°) for statistical robustness
- 3 amplitude scales (0.5x, 1.0x, 2.5x) for non-linearity testing
- Chirp signal validation with continuous frequency sweeps
Dataset Structure
| Column | Description |
|---|---|
family |
Problem family name (always exponential_interest for this dataset) |
question_id |
Question variant ID (0, 1, or 2) |
signal_type |
Type of parameter variation (sinusoid or chirp) |
amplitude_scale |
Scaling factor for parameter variation amplitude |
frequency_cycles |
Frequency of parameter variation in cycles per sweep |
phase_deg |
Starting phase of sinusoidal signal in degrees |
time_step |
Step index within the sweep |
p_value |
The dynamic parameter value for this time step |
prompt |
The complete prompt text for the model |
ground_truth |
The correct numerical answer |
symbolic_baseline_answer |
Answer from perfect symbolic solver |
Usage
from datasets import load_dataset
# Load this specific domain
dataset = load_dataset("cognitive-metrology-lab/MathBode-ExponentialInterest")
# Access the data
print(dataset['train'][0])
Related Datasets
- MathBode - Complete benchmark with all families
- MathBode-LinearSolve
- MathBode-RatioSaturation
- MathBode-ExponentialInterest
- MathBode-LinearSystem
- MathBode-SimilarTriangles
Citation
If you use this dataset, please cite our work (citation to be added).