  Recent studies have shown that Large Language Models (LLMs) have the
potential to process extremely long text. Many works only evaluate LLMs'
long-text processing ability on the language modeling task, with perplexity
(PPL) as the evaluation metric. However, in our study, we find that there is no
correlation between PPL and LLMs' long-text understanding ability. Besides, PPL
may only reflect the model's ability to model local information instead of
catching long-range dependency. Therefore, only using PPL to prove the model
could process long text is inappropriate. The local focus feature of PPL could
also explain some existing phenomena, such as the great extrapolation ability
of the position method ALiBi. When evaluating a model's ability in long text,
we might pay more attention to PPL's limitation and avoid overly relying on it.
