Large language models (LLMs) can generate long-form and coherent text, but
they still frequently hallucinate facts, thus limiting their reliability. To
address this issue, inference-time methods that elicit truthful responses have
been proposed by shifting LLM representations towards learned "truthful
directions". However, applying the truthful directions with the same intensity
fails to generalize across different question contexts. We propose LITO, a
Learnable Intervention method for Truthfulness Optimization that automatically
identifies the optimal intervention intensity tailored to a 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 issues
with one-size-fits-all intervention-based solutions, maximizing model
truthfulness by reflecting internal knowledge only when the model is confident.

LITO 提出了一种可学习的干预方法，通过识别特定语境下的最佳干预强度，提高真实性，并在高度不确定时选择最准确的回答或拒绝回答。