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This study investigates the limitations of steering vectors in controlled text generation by analyzing their generalization capabilities across trait expressibility, task transfer, and multi-trait composition. Using the PLUME benchmark, the authors evaluate steering vectors on summarization and email-writing tasks with two open-source models, revealing substantial variability in steering effectiveness based on the chosen traits. The findings indicate that while steering vectors can guide model outputs, their performance diminishes when transferring vectors between tasks or combining multiple vectors, highlighting significant challenges in their application for preference alignment.
Steering vectors may not be the universal solution for preference-aligned generation, as their effectiveness significantly drops with trait complexity and task transfer.
Steering vectors have emerged as a promising approach to controlled text generation, offering interpretable, training-free mechanisms for shaping model outputs. However, their practical generality remains poorly understood. We study the limits of steering vector generalization along three dimensions: trait expressibility, task transfer, and multi-trait composition. Using the PLUME writing personalization benchmark, we extract steering vectors for a range of preferences and evaluate them on summarization and email-writing tasks across two open-source models (Qwen2.5-7B-Instruct and Llama3.1-8B-Instruct). We find that steering effectiveness varies substantially across traits. We further show that steering effectiveness can degrade when vectors extracted from positive and negative style examples are transferred to downstream writing personalization tasks. Finally, we compare common methods for composing multiple steering vectors and find that all methods suffer significant drops in trait expression as more vectors are added, with a tradeoff between coherence and expressibility that requires per-setting hyperparameter tuning. Taken together, our results suggest that steering vectors face meaningful limits as a general-purpose tool for preference alignment.
