警惕性能差距：特征引导中的能力-行为权衡

Large Language Models (LLMs) often exhibit a significant gap between their inherent capabilities and their actual behaviors when performing complex tasks. This study delves into feature steering, an emerging technique designed to enhance LLM performance by directly intervening in the model's internal representations. We reveal that while feature steering can effectively boost model performance on specific tasks, its impact doesn't always perfectly align with the model's intrinsic capabilities. This capability-behavior gap stems from the possibility that steering operations might inadvertently alter the model's understanding of the task or introduce biases. The paper proposes a theoretical framework to quantify and analyze this trade-off, and we design a series of experiments to validate our hypotheses. Experimental results indicate that excessive or improper steering can cause model behavior to deviate from its optimal capabilities, potentially even harming generalization. We further explore methods to bridge this gap by optimizing steering strategies, integrating few-shot learning, or incorporating external knowledge. This research emphasizes the critical need for careful consideration when applying feature steering, balancing its direct influence on model behavior with its indirect effects on underlying capabilities to avoid introducing new performance bottlenecks. Ultimately, this study offers novel perspectives and practical guidance for understanding and improving steering techniques in LLMs.