arXiv:2606.05079v1 Announce Type: cross 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 distribute
The rapid advancement in LLM capabilities is driving a need for more efficient and precise control mechanisms, making research into steering and interpretability of these models a current priority.
Improved methods for function vectors allow finer-grained control and steerability of LLMs, which is crucial for developing reliable and robust AI applications and agents.
The ability to more efficiently and accurately utilize function vectors could significantly enhance the performance and predictability of LLMs in in-context learning scenarios, potentially leading to more sophisticated AI system deployments.
- · AI developers
- · LLM researchers
- · AI product companies
- · Inefficient LLM steering methods
- · LLM applications requiring extensive manual tuning
Efficient function vectors enable more precise and reliable control over LLM behavior.
This improved control can accelerate the development and deployment of advanced AI agents and specialized LLM applications.
The widespread adoption of steered LLMs could lead to new paradigms in human-AI interaction and task automation.
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Read at arXiv cs.LG