Although large language models (LLMs) have demonstrated impressive text
generation capabilities, they are easily misled by the untruthful context
provided by users or knowledge argumentation tools, thereby producing
hallucinations. To alleviate the LLMs from being misled by untruthful
information and take advantage of knowledge argumentation, we propose
Truth-Aware Context Selection (TACS), a lightweight method to shield untruthful
context from the inputs. TACS begins by performing truth detection on the input
context, leveraging the parameterized knowledge within the LLM. Subsequently,
it constructs a corresponding attention mask based on the truthfulness of each
position, selecting the truthful context and discarding the untruthful context.
Additionally, we introduce a new evaluation metric, Disturbance Adaption Rate,
to further study the LLMs' ability to accept truthful information and resist
untruthful information. Experimental results show that TACS can effectively
filter information in context and significantly improve the overall quality of
LLMs' responses when presented with misleading information.

我们提出了 Truth-Aware Context Selection（TACS）方法，该方法通过对输入上下文进行真实性检测，并基于每个位置的真实性构建相应的注意力掩码，以选择真实的上下文并丢弃虚假的上下文，有效过滤上下文信息并显著提高大型语言模型的整体响应质量。

真实感知上下文选择：减轻大型语言模型被虚假上下文误导的幻觉

Truth-Aware Context Selection: Mitigating the Hallucinations of Large  Language Models Being Misled by Untruthful Contexts

Text-based misinformation permeates online discourses, yet evidence of
people's ability to discern truth from such deceptive textual content is
scarce. We analyze a novel TV game show data where conversations in a
high-stake environment between individuals with conflicting objectives result
in lies. We investigate the manifestation of potentially verifiable language
cues of deception in the presence of objective truth, a distinguishing feature
absent in previous text-based deception datasets. We show that there exists a
class of detectors (algorithms) that have similar truth detection performance
compared to human subjects, even when the former accesses only the language
cues while the latter engages in conversations with complete access to all
potential sources of cues (language and audio-visual). Our model, built on a
large language model, employs a bottleneck framework to learn discernible cues
to determine truth, an act of reasoning in which human subjects often perform
poorly, even with incentives. Our model detects novel but accurate language
cues in many cases where humans failed to detect deception, opening up the
possibility of humans collaborating with algorithms and ameliorating their
ability to detect the truth.

基于一项新型电视游戏节目数据的分析，我们检验了在目标真相存在的情况下，人们辨别文本内容真实性的能力，显示了存在一类能够与人类具有相似真相检测性能的检测器模型，这一模型基于大型语言模型，通过学习可分辨线索来确定真相，该模型在很多情况下能够检测出人类无法察觉的欺骗语言线索，并为与算法的协作提供了可能，进而增强人类的真相检测能力。