README.md

---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: choices
    large_list: string
  - name: label
    dtype: string
  - name: sympy_reprs
    large_list: string
  - name: degrees
    large_list: int32
  - name: question_id
    dtype: int64
  splits:
  - name: train
    num_bytes: 57268567.0
    num_examples: 10000
  - name: validation
    num_bytes: 5700650.0
    num_examples: 1000
  - name: test
    num_bytes: 5747661.0
    num_examples: 1000
  download_size: 57729824
  dataset_size: 68716878.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
license: cc-by-4.0
---

# SymbolicRegressionQA

SymbolicRegressionQA (SRQA) is a multiple-choice visual question answering dataset introduced in
[Bayesian Adaptation Gym](https://arxiv.org/abs/2606.22188) (UAI 2026). Each question shows a plot of a
symbolic expression and asks which of four candidate formulas describes the relationship between the
variables.

## Splits

| Split | Examples |
| --- | --- |
| train | 10,000 |
| validation | 1,000 |
| test | 1,000 |

## Fields

| Field | Type | Description |
| --- | --- | --- |
| `image` | `Image` | 224x224 grayscale plot of the ground truth expression |
| `choices` | `list[str]` | Four candidate formulas, in the order they are presented |
| `label` | `str` | Letter of the correct choice, one of `A`, `B`, `C`, `D` |
| `sympy_reprs` | `list[str]` | SymPy representation of each of the four choices |
| `degrees` | `list[int]` | Polynomial degree of each of the four choices |
| `question_id` | `int` | Unique identifier within the split |

The correct answer is close to uniform over the four letters in every split.

## Usage

```python
from datasets import load_dataset

data = load_dataset("csamplawski/SymbolicRegressionQA", split="test")
```

The prompt used in the BAG experiments is built from `choices` as follows:

```
For the provided image of a plot, which of following formulas best describes the relationship between the variables? Output the letter of your choice only.
Choices:
A) <choices[0]>
B) <choices[1]>
C) <choices[2]>
D) <choices[3]>
```

See `bayesadapt/datasets/srqa.py` in [BayesAdapt](https://github.com/SRI-CSL/BayesAdapt) for the loader
used in the paper.

## Citation

```bibtex
@misc{samplawski2026bayesianadaptationgymbenchmark,
      title={Bayesian Adaptation Gym: A Benchmark for the Bayesian Low-Rank Adaptation of Multi-Modal Language Models}, 
      author={Colin Samplawski and Ramneet Kaur and Manoj Acharya and Anirban Roy and Adam D. Cobb},
      year={2026},
      eprint={2606.22188},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2606.22188}, 
}
```