Exploring Consciousness in LLMs: A Systematic Survey of Theories, Implementations, and Frontier Risks

• URL: http://arxiv.org/abs/2505.19806v1
• Date: Mon, 26 May 2025 10:40:52 GMT
• Title: Exploring Consciousness in LLMs: A Systematic Survey of Theories, Implementations, and Frontier Risks
• Authors: Sirui Chen, Shuqin Ma, Shu Yu, Hanwang Zhang, Shengjie Zhao, Chaochao Lu,
• Abstract summary: Consciousness is one of the most profound and distinguishing features of the human mind.<n>As large language models (LLMs) develop at an unprecedented pace, questions concerning intelligence and consciousness have become increasingly significant.
• Score: 46.93509559847712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Consciousness stands as one of the most profound and distinguishing features of the human mind, fundamentally shaping our understanding of existence and agency. As large language models (LLMs) develop at an unprecedented pace, questions concerning intelligence and consciousness have become increasingly significant. However, discourse on LLM consciousness remains largely unexplored territory. In this paper, we first clarify frequently conflated terminologies (e.g., LLM consciousness and LLM awareness). Then, we systematically organize and synthesize existing research on LLM consciousness from both theoretical and empirical perspectives. Furthermore, we highlight potential frontier risks that conscious LLMs might introduce. Finally, we discuss current challenges and outline future directions in this emerging field. The references discussed in this paper are organized at https://github.com/OpenCausaLab/Awesome-LLM-Consciousness.

Related papers

• How Deep is Love in LLMs' Hearts? Exploring Semantic Size in Human-like Cognition [75.11808682808065]
  This study investigates whether large language models (LLMs) exhibit similar tendencies in understanding semantic size.<n>Our findings reveal that multi-modal training is crucial for LLMs to achieve more human-like understanding.<n> Lastly, we examine whether LLMs are influenced by attention-grabbing headlines with larger semantic sizes in a real-world web shopping scenario.
  arXiv  Detail & Related papers  (2025-03-01T03:35:56Z)
• Knowledge Boundary of Large Language Models: A Survey [75.67848187449418]
  Large language models (LLMs) store vast amount of knowledge in their parameters, but they still have limitations in the memorization and utilization of certain knowledge.<n>This highlights the critical need to understand the knowledge boundary of LLMs, a concept that remains inadequately defined in existing research.<n>We propose a comprehensive definition of the LLM knowledge boundary and introduce a formalized taxonomy categorizing knowledge into four distinct types.
  arXiv  Detail & Related papers  (2024-12-17T02:14:02Z)
• A Perspective on Large Language Models, Intelligent Machines, and Knowledge Acquisition [0.6138671548064355]
  Large Language Models (LLMs) are known for their remarkable ability to generate 'knowledge' However, there is a huge gap between LLM's and human capabilities for understanding abstract concepts and reasoning. We discuss these issues in a larger philosophical context of human knowledge acquisition and the Turing test.
  arXiv  Detail & Related papers  (2024-08-13T03:25:49Z)
• Knowledge Mechanisms in Large Language Models: A Survey and Perspective [88.51320482620679]
  This paper reviews knowledge mechanism analysis from a novel taxonomy including knowledge utilization and evolution.<n>We discuss what knowledge LLMs have learned, the reasons for the fragility of parametric knowledge, and the potential dark knowledge (hypothesis) that will be challenging to address.
  arXiv  Detail & Related papers  (2024-07-22T06:15:59Z)
• Should We Fear Large Language Models? A Structural Analysis of the Human Reasoning System for Elucidating LLM Capabilities and Risks Through the Lens of Heidegger's Philosophy [0.0]
  This study investigates the capabilities and risks of Large Language Models (LLMs) It uses the innovative parallels between the statistical patterns of word relationships within LLMs and Martin Heidegger's concepts of "ready-to-hand" and "present-at-hand" Our findings reveal that while LLMs possess the capability for Direct Explicative Reasoning and Pseudo Rational Reasoning, they fall short in authentic rational reasoning and have no creative reasoning capabilities.
  arXiv  Detail & Related papers  (2024-03-05T19:40:53Z)
• I Think, Therefore I am: Benchmarking Awareness of Large Language Models Using AwareBench [20.909504977779978]
  We introduce AwareBench, a benchmark designed to evaluate awareness in large language models (LLMs) We categorize awareness in LLMs into five dimensions, including capability, mission, emotion, culture, and perspective. Our experiments, conducted on 13 LLMs, reveal that the majority of them struggle to fully recognize their capabilities and missions while demonstrating decent social intelligence.
  arXiv  Detail & Related papers  (2024-01-31T14:41:23Z)
• Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models [116.01843550398183]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks. LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge.
  arXiv  Detail & Related papers  (2023-09-03T16:56:48Z)
• Deception Abilities Emerged in Large Language Models [0.0]
  Large language models (LLMs) are currently at the forefront of intertwining artificial intelligence (AI) systems with human communication and everyday life. This study reveals that such strategies emerged in state-of-the-art LLMs, such as GPT-4, but were non-existent in earlier LLMs. We conduct a series of experiments showing that state-of-the-art LLMs are able to understand and induce false beliefs in other agents.
  arXiv  Detail & Related papers  (2023-07-31T09:27:01Z)
• Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation [109.8527403904657]
  We show that large language models (LLMs) possess unwavering confidence in their knowledge and cannot handle the conflict between internal and external knowledge well. Retrieval augmentation proves to be an effective approach in enhancing LLMs' awareness of knowledge boundaries. We propose a simple method to dynamically utilize supporting documents with our judgement strategy.
  arXiv  Detail & Related papers  (2023-07-20T16:46:10Z)

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.