  Attention is a fundamental component behind the remarkable achievements of
large language models (LLMs). However, our current understanding of the
attention mechanism, especially regarding how attention distributions are
established, remains limited. Inspired by recent studies that explore the
presence of attention sink in the initial token, which receives
disproportionately large attention scores despite their lack of semantic
importance, this work delves deeper into this phenomenon. We aim to provide a
more profound understanding of the existence of attention sinks within LLMs and
to uncover ways to enhance the achievable accuracy of LLMs by directly
optimizing the attention distributions, without the need for weight finetuning.
Specifically, this work begins with comprehensive visualizations of the
attention distributions in LLMs during inference across various inputs and
tasks. Based on these visualizations, to the best of our knowledge, we are the
first to discover that (1) attention sinks occur not only at the start of
sequences but also within later tokens of the input, and (2) not all attention
sinks have a positive impact on the achievable accuracy of LLMs. Building upon
our findings, we propose a training-free Attention Calibration Technique (ACT)
that automatically optimizes the attention distributions on the fly during
inference in an input-adaptive manner. Extensive experiments validate that ACT
consistently enhances the accuracy of various LLMs across different
applications. Specifically, ACT achieves an average improvement of up to 7.30%
in accuracy across different datasets when applied to Llama-30B. Our code is
available at https://github.com/GATECH-EIC/ACT.
