Trusted Source Alignment in Large Language Models
Vasilisa Bashlovkina Zhaobin Kuang Riley Matthews Edward Clifford Yennie Jun William W. Cohen Simon Baumgartner
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.