  In this paper, we identify a new category of bias that induces
input-conflicting hallucinations, where large language models (LLMs) generate
responses inconsistent with the content of the input context. This issue we
have termed the false negative problem refers to the phenomenon where LLMs are
predisposed to return negative judgments when assessing the correctness of a
statement given the context. In experiments involving pairs of statements that
contain the same information but have contradictory factual directions, we
observe that LLMs exhibit a bias toward false negatives. Specifically, the
model presents greater overconfidence when responding with False. Furthermore,
we analyze the relationship between the false negative problem and context and
query rewriting and observe that both effectively tackle false negatives in
LLMs.
