LLMs Do Not Have Human-Like Working Memory
원문 초록
Human working memory is an active cognitive system that enables not only the temporary storage of information but also its processing and utilization. Without working memory, individuals may produce unreal conversations, exhibit self-contradiction, and struggle with tasks requiring mental reasoning. In this paper, we demonstrate that Large Language Models (LLMs) lack this human-like cognitive ability, posing a significant challenge to achieving artificial general intelligence. We validate this claim through three experiments: (1) Number Guessing Game, (2) Yes or No Game, and (3) Math Magic. Experimental results on several model families indicate that current LLMs fail to exhibit human-like cognitive behaviors in these scenarios. By highlighting this limitation, we aim to encourage further research in developing LLMs with improved working memory capabilities.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Personalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing
Reasoning or Memorization? Unreliable Results of Reinforcement Learning Due to Data Contamination
A Practical Two-Stage Recipe for Mathematical LLMs: Maximizing Accuracy with SFT and Efficiency with Reinforcement Learning
The Delta Learning Hypothesis: Preference Tuning on Weak Data can Yield Strong Gains
LLMThinkBench: Towards Basic Math Reasoning and Overthinking in Large Language Models
EvoAgentX: An Automated Framework for Evolving Agentic Workflows
토론
이 논문과 연결된 포럼 글이 아직 없습니다.