  This paper explores the impact of extending input lengths on the capabilities
of Large Language Models (LLMs). Despite LLMs advancements in recent times,
their performance consistency across different input lengths is not well
understood. We investigate this aspect by introducing a novel QA reasoning
framework, specifically designed to assess the impact of input length. We
isolate the effect of input length using multiple versions of the same sample,
each being extended with padding of different lengths, types and locations. Our
findings show a notable degradation in LLMs' reasoning performance at much
shorter input lengths than their technical maximum. We show that the
degradation trend appears in every version of our dataset, although at
different intensities. Additionally, our study reveals that the traditional
metric of next word prediction correlates negatively with performance of LLMs'
on our reasoning dataset. We analyse our results and identify failure modes
that can serve as useful guides for future research, potentially informing
strategies to address the limitations observed in LLMs.
