Large language models (LLMs) are trained on web-scale corpora that inevitably
include contradictory factual information from sources of varying reliability.
In this paper, we propose measuring an LLM property called trusted source
alignment (TSA): the model's propensity to align with content produced by
trusted publishers in the face of uncertainty or controversy. We present
FactCheckQA, a TSA evaluation dataset based on a corpus of fact checking
articles. We describe a simple protocol for evaluating TSA and offer a detailed
analysis of design considerations including response extraction, claim
contextualization, and bias in prompt formulation. Applying the protocol to
PaLM-2, we find that as we scale up the model size, the model performance on
FactCheckQA improves from near-random to up to 80% balanced accuracy in
aligning with trusted sources.

本文提出了大语言模型（LLMs）的一个属性 —— 受信任的来源对齐（TSA），用于衡量模型在面对不确定性或争议时与受信任的发布者内容的一致性。在 FactCheckQA 评估数据集上应用该属性，作者发现随着模型规模的增大，模型在对齐受信任来源方面的性能从随机状态提高到最高 80% 的平衡准确率。