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arXiv · 2606.05079

Fast & Faithful Function Vectors

Minh An Pham·Anton Segeler·Thomas Wiegand·Wojciech Samek·Sebastian Lapuschkin·Patrick Kahardipraja·Reduan Achtibat
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Abstract

Function vectors (FVs) are task representations elicited during in-context learning that can be used to steer Large Language Models (LLMs). However, design choices in their formulation remain underexplored. In this work, we study the impact of varying FV definitions for instructions along two degrees of freedom: attention head selection and steering. For head selection, using gradient-based attributions with Layer-wise Relevance Propagation (LRP) substantially improves efficiency as well as accuracy. For FV steering, applying it in a distributed manner yields a higher accuracy compared to simple aggregation. Our code is publicly available.

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Minh An Pham, Anton Segeler, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin, Patrick Kahardipraja, Reduan Achtibat. 2026-06-03. Fast & Faithful Function Vectors. https://arxiv.org/abs/2606.05079

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