Trusted Source Alignment in Large Language Models

Vasilisa Bashlovkina, Zhaobin Kuang, Riley Matthews, Edward Clifford, yennie jun, William W. Cohen, Simon Baumgartner

Trusted Source Alignment in Large Language Models: 11 upvotes on Hugging Face Daily Papers, #11 of 14 papers on 2023-11-14. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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