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New 'influence score' measures Transformer attention head impact

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new 'influence score' to measure the impact of attention heads within Transformer models, particularly for classification tasks. This score combines directional influence on output logits with structural contributions within the model's residual stream, allowing for analysis at multiple levels. When applied to a DeBERTa model used for prompt injection detection, the framework highlighted differences in decision-making between correct and incorrect predictions, offering a balanced approach between detailed circuit analysis and broader output-based methods. AI

IMPACT Provides a new method for understanding and potentially improving the decision-making processes of Transformer-based classifiers.

RANK_REASON The cluster contains an academic paper detailing a new research methodology for analyzing Transformer models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New 'influence score' measures Transformer attention head impact

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The cluster contains an academic paper detailing a new research methodology for analyzing Transformer models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Lisa Bouger, Yannick Teglia, Philippe Loubet Moundi ·

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

    arXiv:2609.05074v1 Announce Type: new 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 wi…

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