How Much Content Do LLMs Generate That Induces Cognitive Bias in Users?
Journal:
arXiv
Published Date:
Jul 3, 2025
Abstract
Large language models (LLMs) are increasingly integrated into applications
ranging from review summarization to medical diagnosis support, where they
affect human decisions. Even though LLMs perform well in many tasks, they may
also inherit societal or cognitive biases, which can inadvertently transfer to
humans. We investigate when and how LLMs expose users to biased content and
quantify its severity. Specifically, we assess three LLM families in
summarization and news fact-checking tasks, evaluating how much LLMs stay
consistent with their context and/or hallucinate. Our findings show that LLMs
expose users to content that changes the sentiment of the context in 21.86% of
the cases, hallucinates on post-knowledge-cutoff data questions in 57.33% of
the cases, and primacy bias in 5.94% of the cases. We evaluate 18 distinct
mitigation methods across three LLM families and find that targeted
interventions can be effective. Given the prevalent use of LLMs in high-stakes
domains, such as healthcare or legal analysis, our results highlight the need
for robust technical safeguards and for developing user-centered interventions
that address LLM limitations.
