  We introduce Inference-Time Intervention (ITI), a technique designed to
enhance the "truthfulness" of large language models (LLMs). ITI operates by
shifting model activations during inference, following a set of directions
across a limited number of attention heads. This intervention significantly
improves the performance of LLaMA models on the TruthfulQA benchmark. On an
instruction-finetuned LLaMA called Alpaca, ITI improves its truthfulness from
32.5% to 65.1%. We identify a tradeoff between truthfulness and helpfulness and
demonstrate how to balance it by tuning the intervention strength. ITI is
minimally invasive and computationally inexpensive. Moreover, the technique is
data efficient: while approaches like RLHF require extensive annotations, ITI
locates truthful directions using only few hundred examples. Our findings
suggest that LLMs may have an internal representation of the likelihood of
something being true, even as they produce falsehoods on the surface.
