  Large language models (LLMs) show an innate skill for solving language based
tasks. But insights have suggested an inability to adjust for information or
task-solving skills becoming outdated, as their knowledge, stored directly
within their parameters, remains static in time. Tool use helps by offloading
work to systems that the LLM can access through an interface, but LLMs that use
them still must adapt to nonstationary environments for prolonged use, as new
tools can emerge and existing tools can change. Nevertheless, tools require
less specialized knowledge, therefore we hypothesize they are better suited for
continual learning (CL) as they rely less on parametric memory for solving
tasks and instead focus on learning when to apply pre-defined tools. To verify
this, we develop a synthetic benchmark and follow this by aggregating existing
NLP tasks to form a more realistic testing scenario. While we demonstrate
scaling model size is not a solution, regardless of tool usage, continual
learning techniques can enable tool LLMs to both adapt faster while forgetting
less, highlighting their potential as continual learners.
