Towards Practical Tool Usage for Continually Learning LLMs
This addresses the challenge of maintaining up-to-date task-solving capabilities in LLMs for practical applications, though it appears incremental as it builds on existing tool-use and continual learning concepts.
The paper tackles the problem of large language models (LLMs) struggling with outdated knowledge in nonstationary environments by exploring tool usage for continual learning, demonstrating that continual learning techniques enable tool-using LLMs to adapt faster while forgetting less.
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 But insights have suggested an inability to adjust for information or task-solving skills becoming outdated, as their knowledge, stored directly within their parameters, remains static in time.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.