Influence Score and Transformers interpretability: Measure of the Effective Impact of Attention Heads at inference time

Abstract:We propose an influence score to quantify the contribution of attention heads to classification decisions in Transformer-based models designed for prompt injection detection. The score combines directional influence on the logits with structural contribution within the residual stream, enabling a multi-scale analysis at the head, layer, and network levels. Applied to a DeBERTa model specialized for prompt injection detection, our framework reveals distinct decision behaviours between correct and erroneous predictions. Our method provides an effective compromise between fine-grained circuit analysis and global output-based methods, and offers a systematic way to study decision mechanisms in Transformer classifiers.

Computation and Language (cs.CL)

arXiv:2609.05074 [cs.CL]

(or arXiv:2609.05074v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.05074 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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