  Large language models (LLMs), even when specifically trained to process long
input contexts, struggle to capture relevant information located in the middle
of their input. This phenomenon has been known as the lost-in-the-middle
problem. In this work, we make three contributions. First, we set out to
understand the factors that cause this phenomenon. In doing so, we establish a
connection between lost-in-the-middle to LLMs' intrinsic attention bias: LLMs
exhibit a U-shaped attention bias where the tokens at the beginning and at the
end of its input receive higher attention, regardless of their relevance.
Second, we mitigate this positional bias through a calibration mechanism,
found-in-the-middle, that allows the model to attend to contexts faithfully
according to their relevance, even though when they are in the middle. Third,
we show found-in-the-middle not only achieves better performance in locating
relevant information within a long context, but also eventually leads to
improved retrieval-augmented generation (RAG) performance across various tasks,
outperforming existing methods by up to 15 percentage points. These findings
open up future directions in understanding LLM attention bias and its potential
consequences.
