Same Task, More Tokens: the Impact of Input Length on the Reasoning
  Performance of Large Language Models
Mosh Levy Alon Jacoby Yoav Goldberg
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 traditional
perplexity metrics do not correlate with performance of LLMs' in long input
reasoning tasks. 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.