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# Arithmetic Puzzles Dataset
A collection of arithmetic puzzles with heavy use of variable assignment. Current LLMs struggle with variable indirection/multi-hop reasoning, this should be a tough test for them.
Inputs are a list of strings representing variable assignments (`c=a+b`), and the output is the integer answer.
Outputs are filtered to be between [-100, 100], and self-reference/looped dependencies are forbidden.
Splits are named like:
- `train_N` 8k total examples of puzzles with N variables
- `test_N` 2k more examples with N variables
Train/test leakage is prevented: all training examples are filtered out of the test set.
Conceptually the data looks like this:
```python
Input:
a=1
b=2
c=a+b
solve(c)=
Output:
3
```
In actuality it looks like this:
```python
{
"input": ['var_0=1', 'var_1=2', 'var_2=a+b', 'solve(var_2)='],
"output": 3
}
```
### Loading the Dataset
```python
from datasets import load_dataset
# Load the entire dataset
dataset = load_dataset("neurallambda/arithmetic_dataset")
# Load specific splits
train_small = load_dataset("neurallambda/arithmetic_dataset", split="train_10")
test_small = load_dataset("neurallambda/arithmetic_dataset", split="test_10")
```
### Preparing Inputs
To prepare the inputs as concatenated strings, you can do this:
```python
def prepare_input(example):
return {
"input_text": "
".join(example["input"]),
"output": example["output"]
}
# Apply the preparation to a specific split
train_small_prepared = train_small.map(prepare_input)
# Example of using the prepared dataset
for example in train_small_prepared.select(range(5)): # Show first 5 examples
print("Input:", example["input_text"])
print("Output:", example["output"])
print()
```
This will produce output similar to:
```
Input: var_0=5
var_1=2
var_2=-2 + -8
var_3=3
var_4=4
var_5=var_2
var_6=var_3 * 10
var_7=var_2 - var_0
var_8=var_1
var_9=-2 - 9
solve(var_3)=
Output: 3
```
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