Large Knowledge Model: Perspectives and Challenges

• URL: http://arxiv.org/abs/2312.02706v2
• Date: Wed, 26 Jun 2024 16:11:55 GMT
• Title: Large Knowledge Model: Perspectives and Challenges
• Authors: Huajun Chen,
• Abstract summary: emphLarge Language Models (LLMs) epitomize the pre-training of extensive, sequence-based world knowledge into neural networks. This article explores large models through the lens of "knowledge" Considering the intricate nature of human knowledge, we advocate for the creation of emphLarge Knowledge Models (LKM)
• Score: 37.42721596964844
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Humankind's understanding of the world is fundamentally linked to our perception and cognition, with \emph{human languages} serving as one of the major carriers of \emph{world knowledge}. In this vein, \emph{Large Language Models} (LLMs) like ChatGPT epitomize the pre-training of extensive, sequence-based world knowledge into neural networks, facilitating the processing and manipulation of this knowledge in a parametric space. This article explores large models through the lens of "knowledge". We initially investigate the role of symbolic knowledge such as Knowledge Graphs (KGs) in enhancing LLMs, covering aspects like knowledge-augmented language model, structure-inducing pre-training, knowledgeable prompts, structured CoT, knowledge editing, semantic tools for LLM and knowledgeable AI agents. Subsequently, we examine how LLMs can boost traditional symbolic knowledge bases, encompassing aspects like using LLM as KG builder and controller, structured knowledge pretraining, and LLM-enhanced symbolic reasoning. Considering the intricate nature of human knowledge, we advocate for the creation of \emph{Large Knowledge Models} (LKM), specifically engineered to manage diversified spectrum of knowledge structures. This promising undertaking would entail several key challenges, such as disentangling knowledge base from language models, cognitive alignment with human knowledge, integration of perception and cognition, and building large commonsense models for interacting with physical world, among others. We finally propose a five-"A" principle to distinguish the concept of LKM.

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)
• KaLM: Knowledge-aligned Autoregressive Language Modeling via Dual-view Knowledge Graph Contrastive Learning [74.21524111840652]
  This paper proposes textbfKaLM, a textitKnowledge-aligned Language Modeling approach. It fine-tunes autoregressive large language models to align with KG knowledge via the joint objective of explicit knowledge alignment and implicit knowledge alignment. Notably, our method achieves a significant performance boost in evaluations of knowledge-driven tasks.
  arXiv  Detail & Related papers  (2024-12-06T11:08:24Z)
• 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. 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)
• Chain-of-Knowledge: Integrating Knowledge Reasoning into Large Language Models by Learning from Knowledge Graphs [55.317267269115845]
  Chain-of-Knowledge (CoK) is a comprehensive framework for knowledge reasoning. CoK includes methodologies for both dataset construction and model learning. We conduct extensive experiments with KnowReason.
  arXiv  Detail & Related papers  (2024-06-30T10:49:32Z)
• MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines and modalities [0.0]
  We use a Large Language Model (LLM) to distill question-answer pairs from raw sources followed by fine-tuning. The resulting MechGPT LLM foundation model is used in a series of computational experiments to explore its capacity for knowledge retrieval, various language tasks, hypothesis generation, and connecting knowledge across disparate areas.
  arXiv  Detail & Related papers  (2023-10-16T14:29:35Z)
• From task structures to world models: What do LLMs know? [0.0]
  In what sense does a large language model have knowledge? We answer by granting LLMs "instrumental knowledge"; knowledge defined by a certain set of abilities. We then ask how such knowledge is related to the more ordinary, "worldly" knowledge exhibited by human agents, and explore this in terms of the degree to which instrumental knowledge can be said to incorporate the structured world models of cognitive science.
  arXiv  Detail & Related papers  (2023-10-06T14:21:59Z)
• COPEN: Probing Conceptual Knowledge in Pre-trained Language Models [60.10147136876669]
  Conceptual knowledge is fundamental to human cognition and knowledge bases. Existing knowledge probing works only focus on factual knowledge of pre-trained language models (PLMs) and ignore conceptual knowledge. We design three tasks to probe whether PLMs organize entities by conceptual similarities, learn conceptual properties, and conceptualize entities in contexts. For the tasks, we collect and annotate 24k data instances covering 393 concepts, which is COPEN, a COnceptual knowledge Probing bENchmark.
  arXiv  Detail & Related papers  (2022-11-08T08:18:06Z)

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.