  Strategies such as chain-of-thought prompting improve the performance of
large language models (LLMs) on complex reasoning tasks by decomposing input
examples into intermediate steps. However, it remains unclear how to apply such
methods to reason over long input documents, in which both the decomposition
and the output of each intermediate step are non-trivial to obtain. In this
work, we propose PEARL, a prompting framework to improve reasoning over long
documents, which consists of three stages: action mining, plan formulation, and
plan execution. More specifically, given a question about a long document,
PEARL decomposes the question into a sequence of actions (e.g., SUMMARIZE,
FIND_EVENT, FIND_RELATION) and then executes them over the document to obtain
the answer. Each stage of PEARL is implemented via zero-shot or few-shot
prompting of LLMs (in our work, GPT-4) with minimal human input. We evaluate
PEARL on a challenging subset of the QuALITY dataset, which contains questions
that require complex reasoning over long narrative texts. PEARL outperforms
zero-shot and chain-of-thought prompting on this dataset, and ablation
experiments show that each stage of PEARL is critical to its performance.
Overall, PEARL is a first step towards leveraging LLMs to reason over long
documents.
