  While large language models (LLMs) have proven to be effective on a large
variety of tasks, they are also known to hallucinate information. To measure
whether an LLM prefers factually consistent continuations of its input, we
propose a new benchmark called FIB(Factual Inconsistency Benchmark) that
focuses on the task of summarization. Specifically, our benchmark involves
comparing the scores an LLM assigns to a factually consistent versus a
factually inconsistent summary for an input news article. For factually
consistent summaries, we use human-written reference summaries that we manually
verify as factually consistent. To generate summaries that are factually
inconsistent, we generate summaries from a suite of summarization models that
we have manually annotated as factually inconsistent. A model's factual
consistency is then measured according to its accuracy, i.e.\ the proportion of
documents where it assigns a higher score to the factually consistent summary.
To validate the usefulness of FIB, we evaluate 23 large language models ranging
from 1B to 176B parameters from six different model families including BLOOM
and OPT. We find that existing LLMs generally assign a higher score to
factually consistent summaries than to factually inconsistent summaries.
However, if the factually inconsistent summaries occur verbatim in the
document, then LLMs assign a higher score to these factually inconsistent
summaries than factually consistent summaries. We validate design choices in
our benchmark including the scoring method and source of distractor summaries.
Our code and benchmark data can be found at https://github.com/r-three/fib.
