  The context window of large language models has been extended to 128k tokens
or more. However, language models still suffer from position bias and have
difficulty in accessing and using the middle part of the context due to the
lack of attention. We examine the relative position awareness of LLMs and the
feasibility of mitigating disproportional attention through prompting. We
augment the original task instruction with $\texttt{attention instructions}$
that direct language models to allocate more attention towards a selected
segment of the context. We conduct a comprehensive investigation on
multi-document question answering task with both position-based and index-based
instructions. We find that language models do not have relative position
awareness of the context. Nevertheless, they demonstrate the capacity to adapt
attention to a specific segment using matching indexes. Our analysis
contributes to a deeper understanding of position bias in LLMs and provides a
pathway to mitigate this bias by instruction, thus benefiting LLMs in locating
and utilizing relevant information from retrieved documents in RAG
applications.
