  There is a growing line of research on verifying the correctness of language
models' outputs. At the same time, LMs are being used to tackle complex queries
that require reasoning. We introduce CoverBench, a challenging benchmark
focused on verifying LM outputs in complex reasoning settings. Datasets that
can be used for this purpose are often designed for other complex reasoning
tasks (e.g., QA) targeting specific use-cases (e.g., financial tables),
requiring transformations, negative sampling and selection of hard examples to
collect such a benchmark. CoverBench provides a diversified evaluation for
complex claim verification in a variety of domains, types of reasoning,
relatively long inputs, and a variety of standardizations, such as multiple
representations for tables where available, and a consistent schema. We
manually vet the data for quality to ensure low levels of label noise. Finally,
we report a variety of competitive baseline results to show CoverBench is
challenging and has very significant headroom. The data is available at
https://huggingface.co/datasets/google/coverbench .
