  Recent studies have indicated that Large Language Models (LLMs) harbor an
inherent understanding of truthfulness, yet often fail to express fully and
generate false statements. This gap between "knowing" and "telling" poses a
challenge for ensuring the truthfulness of generated content. To address this,
we introduce Adaptive Activation Steering (ACT), a tuning-free method that
adaptively shift LLM's activations in "truthful" direction during inference.
ACT addresses diverse categories of hallucinations by utilizing diverse
steering vectors and adjusting the steering intensity adaptively. Applied as an
add-on across various models, ACT significantly improves truthfulness in LLaMA
($\uparrow$ 142\%), LLaMA2 ($\uparrow$ 24\%), Alpaca ($\uparrow$ 36\%), Vicuna
($\uparrow$ 28\%), and LLaMA2-Chat ($\uparrow$ 19\%). Furthermore, we verify
ACT's scalability across larger models (13B, 33B, 65B), underscoring the
adaptability of ACT to large-scale language models.
