Empowering Working Memory for Large Language Model Agents

Understand

Large language models (LLMs) have achieved impressive linguistic capabilities.

• However, a key limitation persists in their lack of human-like memory faculties.
• LLMs exhibit constrained memory retention across sequential interactions, hindering complex reasoning.
• This paper explores the potential of applying cognitive psychology's working memory frameworks, to enhance LLM architecture.