SquareGrad

Bases: MultitaskExplainerMixin, AttributionExplainer

SquareGrad is a gradient-based attribution method that computes the variance of input gradients under random perturbations. Unlike methods that average gradients (e.g., SmoothGrad), SquareGrad averages the square of the gradient.

Procedure:

• Generate multiple perturbed versions of the input by adding noise (Gaussian) to the input embeddings.
• For each noisy input, compute the gradient of the output with respect to the embeddings.
• Average the square of the gradients across all samples.
• Aggregate the result per token (e.g., by norm with the input) to get the final attribution scores.

Reference: Hooker et al. (2019). A Benchmark for Interpretability Methods in Deep Neural Networks. Paper

Examples:

>>> from interpreto import SquareGrad
>>> method = SquareGrad(model, tokenizer, batch_size=4,
>>>                     n_perturbations=50, noise_std=0.01)
>>> explanations = method.explain(text)

Parameters:

Name	Type	Description	Default
model	PreTrainedModel	model to explain	required
tokenizer	PreTrainedTokenizer	Hugging Face tokenizer associated with the model	required
batch_size	int	batch size for the attribution method	4
granularity	Granularity	The level of granularity for the explanation. Options are: ALL_TOKENS, TOKEN, WORD, or SENTENCE. Defaults to Granularity.WORD. To obtain it, from interpreto import Granularity then Granularity.WORD.	WORD
granularity_aggregation_strategy	GranularityAggregationStrategy	how to aggregate token-level attributions into granularity scores. Options are: MEAN, MAX, MIN, SUM, and SIGNED_MAX. Ignored for granularity set to ALL_TOKENS or TOKEN.	MEAN
device	device	device on which the attribution method will be run	None
inference_mode	Callable[[Tensor], Tensor]	The mode used for inference. It can be either one of LOGITS, SOFTMAX, or LOG_SOFTMAX. Use InferenceModes to choose the appropriate mode.	LOGITS
input_x_gradient	bool	If True, multiplies the input embeddings with their gradients before aggregation. Defaults to True.	True
n_perturbations	int	the number of interpolations to generate	10
noise_std	float	standard deviation of the Gaussian noise to add to the inputs	0.1