Large Language Models (LLMs) have revolutionized numerous applications, making them an integral part of our digital ecosystem. However, their reliability becomes critical, especially when these models are exposed to misinformation. We primarily analyze the susceptibility of state-of-the-art LLMs to factual inaccuracies when they encounter false information in a QnA scenario, an issue that can lead to a phenomenon we refer to as *knowledge drift*, which significantly undermines the trustworthiness of these models. We evaluate the factuality and the uncertainty of the models' responses relying on Entropy, Perplexity, and Token Probability metrics. Our experiments reveal that an LLM's uncertainty can increase up to 56.6% when the question is answered incorrectly due to the exposure to false information. At the same time, repeated exposure to the same false information can decrease the models uncertainty again (-52.8% w.r.t. the answers on the untainted prompts), potentially manipulating the underlying model's beliefs and introducing a drift from its original knowledge. These findings provide insights into LLMs' robustness and vulnerability to adversarial inputs, paving the way for developing more reliable LLM applications across various domains. The code is available at https://github.com/afastowski/knowledge_drift.

本研究解决了大型语言模型在接触错误信息时所产生的知识漂移问题。通过对模型在问答场景中对虚假信息反应的深入分析，提出了一种结合熵、困惑度和标记概率度量的方法。研究发现，模型在接触错误信息时，其不确定性可能增加高达56.6%，重复接触同一错误信息又可能导致不确定性降低，影响模型的原始知识，推动了对大型语言模型可应用性的可靠性发展。

通过错误信息理解大型语言模型中的知识漂移