Large Language Models (LLM) are prone to returning false information. It
constitutes one of major challenges in the AI field. In our work, we explore
paradigm introduced by Inference-Time-Intervention (ITI). In first stage, it
identifies attention heads, which contain the highest amount of desired type of
knowledge (e.g., truthful). Afterwards, during inference, LLM activations are
shifted for chosen subset of attention heads. We further improved the ITI
framework by introducing a nonlinear probing and multi-token intervention -
Non-Linear ITI (NL-ITI). NL-ITI is tested on diverse multiple-choice
benchmarks, including TruthfulQA, on which we report around 14% MC1 metric
improvement with respect to the baseline ITI results. NL-ITI achieves also
encouraging results on other testsets - on Business Ethics subdomain of MMLU,
around 18% MC1 improvement over baseline LLaMA2-7B. Additionally, NL-ITI
performs better while being less invasive in the behavior of LLM at the same
time (as measured by Kullback-Leibler divergence).

大型语言模型容易返回虚假信息，为了解决这个问题，该研究探索了一种介入推理时间的范式，引入了非线性探测和多标记介入方法，称之为 Non-Linear ITI。该方法在多种多项选择基准测试中取得了鼓舞人心的结果，并且在与其他基线和模型相比的实验中表现得更为非侵入性。