  Large language models (LLMs) can generate long-form and coherent text, yet
they often hallucinate facts, which undermines their reliability. To mitigate
this issue, inference-time methods steer LLM representations toward the
"truthful directions" previously learned for truth elicitation. However,
applying these truthful directions with the same intensity fails to generalize
across different query contexts. We propose LITO, a Learnable Intervention
method for Truthfulness Optimization that automatically identifies the optimal
intervention intensity tailored to each specific context. LITO explores a
sequence of model generations based on increasing levels of intervention
intensities. It selects the most accurate response or refuses to answer when
the predictions are highly uncertain. Experiments on multiple LLMs and
question-answering datasets demonstrate that LITO improves truthfulness while
preserving task accuracy. The adaptive nature of LITO counters the limitations
of one-size-fits-all intervention methods, maximizing truthfulness by
reflecting the model's internal knowledge only when it is confident. Our code
is available at https://github.com/launchnlp/LITO.
