  Long-form generations from large language models (LLMs) contain a mix of
factual and non-factual claims, making evaluating factuality difficult. Prior
works evaluate the factuality of a long paragraph by decomposing it into
multiple facts, verifying those facts independently, and aggregating the
results. Such methods assume that combining factual claims forms a factual
paragraph. The above assumption can be violated: we show that strong
open-source models like Llama-chat can generate paragraphs that contain
verifiable facts, but the facts are combined into a non-factual paragraph due
to entity ambiguity. We further reveal that existing factuality metrics,
including FActScore and citation recall, cannot properly evaluate these
non-factual paragraphs and overestimate their factuality. To address this, we
introduce an enhanced metric, D-FActScore, specifically designed for content
with ambiguous entities. We evaluate the D-FActScores of people biographies
generated by retrieval-augmented LLMs. We show that D-FActScore can better
assess the factuality of paragraphs with entity ambiguity than FActScore. We
also find that four widely used open-source LLMs tend to mix information of
distinct entities to form non-factual paragraphs, making their D-FActScore much
lower than FActScore by over 10%.
