CogLM: Tracking Cognitive Development of Large Language Models

• URL: http://arxiv.org/abs/2408.09150v1
• Date: Sat, 17 Aug 2024 09:49:40 GMT
• Title: CogLM: Tracking Cognitive Development of Large Language Models
• Authors: Xinglin Wang, Peiwen Yuan, Shaoxiong Feng, Yiwei Li, Boyuan Pan, Heda Wang, Yao Hu, Kan Li,
• Abstract summary: We construct a benchmark CogLM based on Piaget's Theory of Cognitive Development. CogLM comprises 1,220 questions spanning 10 cognitive abilities crafted by more than 20 human experts. We find that human-like cognitive abilities have emerged in advanced LLMs (GPT-4), comparable to those of a 20-year-old human.
• Score: 20.138831477848615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Piaget's Theory of Cognitive Development (PTC) posits that the development of cognitive levels forms the foundation for human learning across various abilities. As Large Language Models (LLMs) have recently shown remarkable abilities across a wide variety of tasks, we are curious about the cognitive levels of current LLMs: to what extent they have developed and how this development has been achieved. To this end, we construct a benchmark CogLM (Cognitive Ability Evaluation for Language Model) based on PTC to assess the cognitive levels of LLMs. CogLM comprises 1,220 questions spanning 10 cognitive abilities crafted by more than 20 human experts, providing a comprehensive testbed for the cognitive levels of LLMs. Through extensive experiments across multiple mainstream LLMs with CogLM, we find that: (1) Human-like cognitive abilities have emerged in advanced LLMs (GPT-4), comparable to those of a 20-year-old human. (2) The parameter size and optimization objective are two key factors affecting the cognitive levels of LLMs. (3) The performance on downstream tasks is positively correlated with the level of cognitive abilities. These findings fill the gap in research on the cognitive abilities of LLMs, tracing the development of LLMs from a cognitive perspective and guiding the future direction of their evolution.

Related papers

• 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 [11.19619695546899]
  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)
• Generative AI as a metacognitive agent: A comparative mixed-method study with human participants on ICF-mimicking exam performance [0.0]
  This study investigates the metacognitive capabilities of Large Language Models relative to human metacognition in the context of the International Coaching Federation ICF exam. Using a mixed method approach, we assessed the metacognitive performance of human participants and five advanced LLMs. The results indicate that LLMs outperformed humans across all metacognitive metrics, particularly in terms of reduced overconfidence, compared to humans.
  arXiv  Detail & Related papers  (2024-05-07T22:15:12Z)
• A Survey on Self-Evolution of Large Language Models [116.54238664264928]
  Large language models (LLMs) have significantly advanced in various fields and intelligent agent applications. To address this issue, self-evolution approaches that enable LLMs to autonomously acquire, refine, and learn from experiences generated by the model itself are rapidly growing.
  arXiv  Detail & Related papers  (2024-04-22T17:43:23Z)
• FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition [56.76951887823882]
  Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
  arXiv  Detail & Related papers  (2024-02-29T21:05:37Z)
• CogGPT: Unleashing the Power of Cognitive Dynamics on Large Language Models [24.079412787914993]
  We propose the concept of the cognitive dynamics of large language models (LLMs) and present a corresponding task with the inspiration of longitudinal studies. Towards the task, we develop CogBench, a novel benchmark to assess the cognitive dynamics of LLMs and validate it through participant surveys. We introduce CogGPT for the task, which features an innovative iterative cognitive mechanism aimed at enhancing lifelong cognitive dynamics.
  arXiv  Detail & Related papers  (2024-01-06T03:59:59Z)
• Exploring the Cognitive Knowledge Structure of Large Language Models: An Educational Diagnostic Assessment Approach [50.125704610228254]
  Large Language Models (LLMs) have not only exhibited exceptional performance across various tasks, but also demonstrated sparks of intelligence. Recent studies have focused on assessing their capabilities on human exams and revealed their impressive competence in different domains. We conduct an evaluation using MoocRadar, a meticulously annotated human test dataset based on Bloom taxonomy.
  arXiv  Detail & Related papers  (2023-10-12T09:55:45Z)
• Revisiting the Reliability of Psychological Scales on Large Language Models [62.57981196992073]
  This study aims to determine the reliability of applying personality assessments to Large Language Models. Analysis of 2,500 settings per model, including GPT-3.5, GPT-4, Gemini-Pro, and LLaMA-3.1, reveals that various LLMs show consistency in responses to the Big Five Inventory.
  arXiv  Detail & Related papers  (2023-05-31T15:03:28Z)
• Do Large Language Models Know What They Don't Know? [74.65014158544011]
  Large language models (LLMs) have a wealth of knowledge that allows them to excel in various Natural Language Processing (NLP) tasks. Despite their vast knowledge, LLMs are still limited by the amount of information they can accommodate and comprehend. This study aims to evaluate LLMs' self-knowledge by assessing their ability to identify unanswerable or unknowable questions.
  arXiv  Detail & Related papers  (2023-05-29T15:30:13Z)

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.