Exploring the Impact of Input Length on Reasoning Performance of Language Models

In this episode, we discuss Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models by Mosh Levy, Alon Jacoby, Yoav Goldberg. The paper explores how the reasoning abilities of Large Language Models (LLMs) are impacted by increasing input lengths, utilizing a specialized QA reasoning framework to examine how performance is influenced by various input sizes. The findings reveal a noticeable drop in performance occurring at shorter input lengths than the maximum specified limits of the models, and across different datasets. It further points out the discrepancy between the models' performance on reasoning tasks with long inputs and the traditional perplexity metrics, suggesting opportunities for further research to overcome these limitations.