Working Memory Capacity of ChatGPT: An Empirical Study
- URL: http://arxiv.org/abs/2305.03731v4
- Date: Thu, 1 Feb 2024 21:39:50 GMT
- Title: Working Memory Capacity of ChatGPT: An Empirical Study
- Abstract summary: We systematically assess the working memory capacity of ChatGPT, a large language model developed by OpenAI.
Our experiments reveal that ChatGPT has a working memory capacity limit strikingly similar to that of humans.
- Score: 10.34965407119012
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Working memory is a critical aspect of both human intelligence and artificial
intelligence, serving as a workspace for the temporary storage and manipulation
of information. In this paper, we systematically assess the working memory
capacity of ChatGPT, a large language model developed by OpenAI, by examining
its performance in verbal and spatial n-back tasks under various conditions.
Our experiments reveal that ChatGPT has a working memory capacity limit
strikingly similar to that of humans. Furthermore, we investigate the impact of
different instruction strategies on ChatGPT's performance and observe that the
fundamental patterns of a capacity limit persist. From our empirical findings,
we propose that n-back tasks may serve as tools for benchmarking the working
memory capacity of large language models and hold potential for informing
future efforts aimed at enhancing AI working memory.
Related papers
- Emerging Human-like Strategies for Semantic Memory Foraging in Large Language Models [0.8749675983608171]
Both humans and Large Language Models (LLMs) store a vast repository of semantic memories.<n>In humans, efficient and strategic access to this memory store is a critical foundation for a variety of cognitive functions.
arXiv Detail & Related papers (2026-03-02T12:55:51Z) - Rethinking Memory Mechanisms of Foundation Agents in the Second Half: A Survey [211.01908189012184]
Memory, with hundreds of papers released this year, emerges as the critical solution to fill the utility gap.<n>We provide a unified view of foundation agent memory along three dimensions.<n>We then analyze how memory is instantiated and operated under different agent topologies.
arXiv Detail & Related papers (2026-01-14T07:38:38Z) - Memory in the Age of AI Agents [217.9368190980982]
This work aims to provide an up-to-date landscape of current agent memory research.<n>We identify three dominant realizations of agent memory, namely token-level, parametric, and latent memory.<n>To support practical development, we compile a comprehensive summary of memory benchmarks and open-source frameworks.
arXiv Detail & Related papers (2025-12-15T17:22:34Z) - ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory [57.517214479414726]
ReasoningBank is a memory framework that distills generalizable reasoning strategies from an agent's self-judged successful and failed experiences.<n>At test time, an agent retrieves relevant memories from ReasoningBank to inform its interaction and then integrates new learnings back, enabling it to become more capable over time.<n>We introduce memory-aware test-time scaling (MaTTS), which accelerates and diversifies this learning process by scaling up the agent's interaction experience.
arXiv Detail & Related papers (2025-09-29T17:51:03Z) - Cognitive Workspace: Active Memory Management for LLMs -- An Empirical Study of Functional Infinite Context [0.0]
Large Language Models (LLMs) face fundamental limitations in context management.<n>We propose Cognitive Workspace, a novel paradigm that transcends traditional Retrieval-Augmented Generation (RAG)<n> Cognitive Workspace addresses these limitations through three core innovations: (1) active memory management with deliberate information curation, (2) hierarchical cognitive buffers enabling persistent working states, and (3) task-driven context optimization.
arXiv Detail & Related papers (2025-08-08T16:32:47Z) - FindingDory: A Benchmark to Evaluate Memory in Embodied Agents [49.89792845476579]
We introduce a new benchmark for long-range embodied tasks in the Habitat simulator.<n>This benchmark evaluates memory-based capabilities across 60 tasks requiring sustained engagement and contextual awareness.
arXiv Detail & Related papers (2025-06-18T17:06:28Z) - How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior [49.62361184944454]
Memory is a critical component in large language model (LLM)-based agents.<n>We study how memory management choices impact the LLM agents' behavior, especially their long-term performance.
arXiv Detail & Related papers (2025-05-21T22:35:01Z) - Do Language Models Understand the Cognitive Tasks Given to Them? Investigations with the N-Back Paradigm [9.577716124021029]
We argue that GPT 3.5's declining performance on 2-back and 3-back tasks reflects a working memory capacity limit similar to humans.
By analyzing a range of open-source language models of varying performance levels on these tasks, we show that the poor performance instead reflects a limitation in task comprehension and task set maintenance.
arXiv Detail & Related papers (2024-12-24T03:06:52Z) - Metacognitive Monitoring: A Human Ability Beyond Generative Artificial Intelligence [0.0]
Large language models (LLMs) have shown impressive alignment with human cognitive processes.
This study investigates whether ChatGPT possess metacognitive monitoring abilities akin to humans.
arXiv Detail & Related papers (2024-10-17T09:42:30Z) - Stable Hadamard Memory: Revitalizing Memory-Augmented Agents for Reinforcement Learning [64.93848182403116]
Current deep-learning memory models struggle in reinforcement learning environments that are partially observable and long-term.
We introduce the Stable Hadamard Memory, a novel memory model for reinforcement learning agents.
Our approach significantly outperforms state-of-the-art memory-based methods on challenging partially observable benchmarks.
arXiv Detail & Related papers (2024-10-14T03:50:17Z) - Learning Symbolic Task Representation from a Human-Led Demonstration: A Memory to Store, Retrieve, Consolidate, and Forget Experiences [3.0501524254444767]
We present a symbolic learning framework inspired by cognitive-like memory functionalities.
Our main contribution is the formalisation of a framework that can be used to investigate different memorises for bootstrapping hierarchical knowledge representations.
arXiv Detail & Related papers (2024-04-16T14:14:34Z) - Towards a Psychology of Machines: Large Language Models Predict Human Memory [0.0]
Large language models (LLMs) have shown remarkable abilities in natural language processing.
This study explores whether LLMs can predict human memory performance in tasks involving garden-path sentences and contextual information.
arXiv Detail & Related papers (2024-03-08T08:41:14Z) - In-Memory Learning: A Declarative Learning Framework for Large Language
Models [56.62616975119192]
We propose a novel learning framework that allows agents to align with their environment without relying on human-labeled data.
This entire process transpires within the memory components and is implemented through natural language.
We demonstrate the effectiveness of our framework and provide insights into this problem.
arXiv Detail & Related papers (2024-03-05T08:25:11Z) - Personalized Large Language Model Assistant with Evolving Conditional Memory [15.780762727225122]
We present a plug-and-play framework that could facilitate personalized large language model assistants with evolving conditional memory.
The personalized assistant focuses on intelligently preserving the knowledge and experience from the history dialogue with the user.
arXiv Detail & Related papers (2023-12-22T02:39:15Z) - Exploring Memorization in Fine-tuned Language Models [53.52403444655213]
We conduct the first comprehensive analysis to explore language models' memorization during fine-tuning across tasks.
Our studies with open-sourced and our own fine-tuned LMs across various tasks indicate that memorization presents a strong disparity among different fine-tuning tasks.
We provide an intuitive explanation of this task disparity via sparse coding theory and unveil a strong correlation between memorization and attention score distribution.
arXiv Detail & Related papers (2023-10-10T15:41:26Z) - Towards A Unified Agent with Foundation Models [18.558328028366816]
We investigate how to embed and leverage such abilities in Reinforcement Learning (RL) agents.
We design a framework that uses language as the core reasoning tool, exploring how this enables an agent to tackle a series of fundamental RL challenges.
We demonstrate substantial performance improvements over baselines in exploration efficiency and ability to reuse data from offline datasets.
arXiv Detail & Related papers (2023-07-18T22:37:30Z) - Think Before You Act: Decision Transformers with Working Memory [44.18926449252084]
Decision Transformer-based decision-making agents have shown the ability to generalize across multiple tasks.
We argue that this inefficiency stems from the forgetting phenomenon, in which a model memorizes its behaviors in parameters throughout training.
We propose a working memory module to store, blend, and retrieve information for different downstream tasks.
arXiv Detail & Related papers (2023-05-24T01:20:22Z) - RET-LLM: Towards a General Read-Write Memory for Large Language Models [53.288356721954514]
RET-LLM is a novel framework that equips large language models with a general write-read memory unit.
Inspired by Davidsonian semantics theory, we extract and save knowledge in the form of triplets.
Our framework exhibits robust performance in handling temporal-based question answering tasks.
arXiv Detail & Related papers (2023-05-23T17:53:38Z) - CogNGen: Constructing the Kernel of a Hyperdimensional Predictive
Processing Cognitive Architecture [79.07468367923619]
We present a new cognitive architecture that combines two neurobiologically plausible, computational models.
We aim to develop a cognitive architecture that has the power of modern machine learning techniques.
arXiv Detail & Related papers (2022-03-31T04:44:28Z) - Reasoning over Vision and Language: Exploring the Benefits of
Supplemental Knowledge [59.87823082513752]
This paper investigates the injection of knowledge from general-purpose knowledge bases (KBs) into vision-and-language transformers.
We empirically study the relevance of various KBs to multiple tasks and benchmarks.
The technique is model-agnostic and can expand the applicability of any vision-and-language transformer with minimal computational overhead.
arXiv Detail & Related papers (2021-01-15T08:37:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.