Exploring the Cognitive Knowledge Structure of Large Language Models: An Educational Diagnostic Assessment Approach

• URL: http://arxiv.org/abs/2310.08172v2
• Date: Wed, 18 Oct 2023 11:37:43 GMT
• Title: Exploring the Cognitive Knowledge Structure of Large Language Models: An Educational Diagnostic Assessment Approach
• Authors: Zheyuan Zhang, Jifan Yu, Juanzi Li, Lei Hou
• Abstract summary: 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.
• Score: 50.125704610228254
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. However, cognitive research on the overall knowledge structure of LLMs is still lacking. In this paper, based on educational diagnostic assessment method, we conduct an evaluation using MoocRadar, a meticulously annotated human test dataset based on Bloom Taxonomy. We aim to reveal the knowledge structures of LLMs and gain insights of their cognitive capabilities. This research emphasizes the significance of investigating LLMs' knowledge and understanding the disparate cognitive patterns of LLMs. By shedding light on models' knowledge, researchers can advance development and utilization of LLMs in a more informed and effective manner.

Related papers

• Refine Knowledge of Large Language Models via Adaptive Contrastive Learning [54.61213933999464]
  A mainstream category of methods is to reduce hallucinations by optimizing the knowledge representation of Large Language Models. We believe that the process of models refining knowledge can greatly benefit from the way humans learn. In our work, by imitating the human learning process, we design an Adaptive Contrastive Learning strategy.
  arXiv  Detail & Related papers  (2025-02-11T02:19:13Z)
• Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs [64.9693406713216]
  Internal mechanisms that contribute to the effectiveness of RAG systems remain underexplored. Our experiments reveal that several core groups of experts are primarily responsible for RAG-related behaviors. We propose several strategies to enhance RAG's efficiency and effectiveness through expert activation.
  arXiv  Detail & Related papers  (2024-10-20T16:08:54Z)
• Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challenges [14.739357670600102]
  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)
• Large Language Models are Limited in Out-of-Context Knowledge Reasoning [65.72847298578071]
  Large Language Models (LLMs) possess extensive knowledge and strong capabilities in performing in-context reasoning. This paper focuses on a significant aspect of out-of-context reasoning: Out-of-Context Knowledge Reasoning (OCKR), which is to combine multiple knowledge to infer new knowledge.
  arXiv  Detail & Related papers  (2024-06-11T15:58:59Z)
• A Comprehensive Study of Knowledge Editing for Large Language Models [82.65729336401027]
  Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. This paper defines the knowledge editing problem and provides a comprehensive review of cutting-edge approaches. We introduce a new benchmark, KnowEdit, for a comprehensive empirical evaluation of representative knowledge editing approaches.
  arXiv  Detail & Related papers  (2024-01-02T16:54:58Z)
• Explore the Potential of LLMs in Misinformation Detection: An Empirical Study [24.256183538265525]
  Large Language Models (LLMs) have garnered significant attention for their powerful ability in natural language understanding and reasoning. This study stands as the pioneering investigation into the understanding capabilities of multiple LLMs regarding both content and propagation across social media platforms.
  arXiv  Detail & Related papers  (2023-11-21T16:03:51Z)
• EpiK-Eval: Evaluation for Language Models as Epistemic Models [16.485951373967502]
  We introduce EpiK-Eval, a novel question-answering benchmark tailored to evaluate LLMs' proficiency in formulating a coherent and consistent knowledge representation from segmented narratives. We argue that these shortcomings stem from the intrinsic nature of prevailing training objectives. The findings from this study offer insights for developing more robust and reliable LLMs.
  arXiv  Detail & Related papers  (2023-10-23T21:15:54Z)
• 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.