  Retrieving external knowledge and prompting large language models with
relevant information is an effective paradigm to enhance the performance of
question-answering tasks. Previous research typically handles paragraphs from
external documents in isolation, resulting in a lack of context and ambiguous
references, particularly in multi-document and complex tasks. To overcome these
challenges, we propose a new retrieval framework IIER, that leverages
Inter-chunk Interactions to Enhance Retrieval. This framework captures the
internal connections between document chunks by considering three types of
interactions: structural, keyword, and semantic. We then construct a unified
Chunk-Interaction Graph to represent all external documents comprehensively.
Additionally, we design a graph-based evidence chain retriever that utilizes
previous paths and chunk interactions to guide the retrieval process. It
identifies multiple seed nodes based on the target question and iteratively
searches for relevant chunks to gather supporting evidence. This retrieval
process refines the context and reasoning chain, aiding the large language
model in reasoning and answer generation. Extensive experiments demonstrate
that IIER outperforms strong baselines across four datasets, highlighting its
effectiveness in improving retrieval and reasoning capabilities.
