Roles of LLMs in the Overall Mental Architecture

• URL: http://arxiv.org/abs/2410.20037v1
• Date: Sat, 26 Oct 2024 01:13:44 GMT
• Title: Roles of LLMs in the Overall Mental Architecture
• Authors: Ron Sun,
• Abstract summary: We may examine the human mental (cognitive/psychological) architecture, and its components and structures. It is argued that, within the human mental architecture, existing LLMs correspond well with implicit mental processes.
• Score: 0.32634122554913997
• License:
• Abstract: To better understand existing LLMs, we may examine the human mental (cognitive/psychological) architecture, and its components and structures. Based on psychological, philosophical, and cognitive science literatures, it is argued that, within the human mental architecture, existing LLMs correspond well with implicit mental processes (intuition, instinct, and so on). However, beyond such implicit processes, explicit processes (with better symbolic capabilities) are also present within the human mental architecture, judging from psychological, philosophical, and cognitive science literatures. Various theoretical and empirical issues and questions in this regard are explored. Furthermore, it is argued that existing dual-process computational cognitive architectures (models of the human cognitive/psychological architecture) provide usable frameworks for fundamentally enhancing LLMs by introducing dual processes (both implicit and explicit) and, in the meantime, can also be enhanced by LLMs. The results are synergistic combinations (in several different senses simultaneously).

Related papers

• Brain-like Functional Organization within Large Language Models [58.93629121400745]
  The human brain has long inspired the pursuit of artificial intelligence (AI) Recent neuroimaging studies provide compelling evidence of alignment between the computational representation of artificial neural networks (ANNs) and the neural responses of the human brain to stimuli. In this study, we bridge this gap by directly coupling sub-groups of artificial neurons with functional brain networks (FBNs) This framework links the AN sub-groups to FBNs, enabling the delineation of brain-like functional organization within large language models (LLMs)
  arXiv  Detail & Related papers  (2024-10-25T13:15:17Z)
• CogniDual Framework: Self-Training Large Language Models within a Dual-System Theoretical Framework for Improving Cognitive Tasks [39.43278448546028]
  Kahneman's dual-system theory elucidates the human decision-making process, distinguishing between the rapid, intuitive System 1 and the deliberative, rational System 2. Recent advancements have positioned large language Models (LLMs) as formidable tools nearing human-level proficiency in various cognitive tasks. This study introduces the textbfCogniDual Framework for LLMs (CFLLMs), designed to assess whether LLMs can, through self-training, evolve from deliberate deduction to intuitive responses.
  arXiv  Detail & Related papers  (2024-09-05T09:33:24Z)
• Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challenges [12.390859712280324]
  This comprehensive review explores the intersection of Large Language Models (LLMs) and cognitive science. We analyze methods for evaluating LLMs cognitive abilities and discuss their potential as cognitive models. We assess cognitive biases and limitations of LLMs, along with proposed methods for improving their performance.
  arXiv  Detail & Related papers  (2024-09-04T02:30:12Z)
• Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making [51.737762570776006]
  LLM-ACTR is a novel neuro-symbolic architecture that provides human-aligned and versatile decision-making. Our framework extracts and embeds knowledge of ACT-R's internal decision-making process as latent neural representations. Our experiments on novel Design for Manufacturing tasks show both improved task performance as well as improved grounded decision-making capability.
  arXiv  Detail & Related papers  (2024-08-17T11:49:53Z)
• Psychomatics -- A Multidisciplinary Framework for Understanding Artificial Minds [0.319565400223685]
  This paper introduces Psychomatics, a framework bridging cognitive science, linguistics, and computer science. It aims to better understand the high-level functioning of LLMs. Psychomatics holds the potential to yield transformative insights into the nature of language, cognition, and intelligence.
  arXiv  Detail & Related papers  (2024-07-23T12:53:41Z)
• Quantifying AI Psychology: A Psychometrics Benchmark for Large Language Models [57.518784855080334]
  Large Language Models (LLMs) have demonstrated exceptional task-solving capabilities, increasingly adopting roles akin to human-like assistants. This paper presents a framework for investigating psychology dimension in LLMs, including psychological identification, assessment dataset curation, and assessment with results validation. We introduce a comprehensive psychometrics benchmark for LLMs that covers six psychological dimensions: personality, values, emotion, theory of mind, motivation, and intelligence.
  arXiv  Detail & Related papers  (2024-06-25T16:09:08Z)
• DeepThought: An Architecture for Autonomous Self-motivated Systems [1.6385815610837167]
  We argue that the internal architecture of large language models (LLMs) cannot support intrinsic motivations, agency, or some degree of consciousness. We propose to integrate LLMs into an architecture for cognitive language agents able to exhibit properties akin to agency, self-motivation, even some features of meta-cognition.
  arXiv  Detail & Related papers  (2023-11-14T21:20:23Z)
• Synergistic Integration of Large Language Models and Cognitive Architectures for Robust AI: An Exploratory Analysis [12.9222727028798]
  This paper explores the integration of two AI subdisciplines employed in the development of artificial agents that exhibit intelligent behavior: Large Language Models (LLMs) and Cognitive Architectures (CAs) We present three integration approaches, each grounded in theoretical models and supported by preliminary empirical evidence. These approaches aim to harness the strengths of both LLMs and CAs, while mitigating their weaknesses, thereby advancing the development of more robust AI systems.
  arXiv  Detail & Related papers  (2023-08-18T21:42:47Z)
• Machine Psychology [54.287802134327485]
  We argue that a fruitful direction for research is engaging large language models in behavioral experiments inspired by psychology. We highlight theoretical perspectives, experimental paradigms, and computational analysis techniques that this approach brings to the table. It paves the way for a "machine psychology" for generative artificial intelligence (AI) that goes beyond performance benchmarks.
  arXiv  Detail & Related papers  (2023-03-24T13:24:41Z)
• Kernel Based Cognitive Architecture for Autonomous Agents [91.3755431537592]
  This paper considers an evolutionary approach to creating a cognitive functionality. We consider a cognitive architecture which ensures the evolution of the agent on the basis of Symbol Emergence Problem solution.
  arXiv  Detail & Related papers  (2022-07-02T12:41:32Z)
• Understanding Deep Architectures with Reasoning Layer [60.90906477693774]
  We show that properties of the algorithm layers, such as convergence, stability, and sensitivity, are intimately related to the approximation and generalization abilities of the end-to-end model. Our theory can provide useful guidelines for designing deep architectures with reasoning layers.
  arXiv  Detail & Related papers  (2020-06-24T00:26:35Z)

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.