Large Knowledge Model: Perspectives and Challenges
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.
Code (0)
등록된 구현이 없습니다.
Tasks
knowledge editingKnowledge GraphsLanguage ModellingmodelWorld KnowledgeMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models
As large language models (LLMs) continue to advance, there is a growing urgency to enhance the interpretability of their internal knowledge mechanisms. Consequently, many interpretation methods have emerged, aiming to un…
Where Common Knowledge Cannot Be Formed, Common Belief Can -- Planning with Multi-Agent Belief Using Group Justified Perspectives
Epistemic planning is the sub-field of AI planning that focuses on changing knowledge and belief. It is important in both multi-agent domains where agents need to have knowledge/belief regarding the environment, but also…
On the Multiple Roles of Ontologies in Explainable AI
This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in Explainable AI and in the development of human-centric explainable systems and intelligible explanations. We conside…
Common Sense ReasoningManagementPositionFillers in Spoken Language Understanding: Computational and Psycholinguistic Perspectives
Disfluencies (i.e. interruptions in the regular flow of speech), are ubiquitous to spoken discourse. Fillers ("uh", "um") are disfluencies that occur the most frequently compared to other kinds of disfluencies. Yet, to t…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)speech-recognitionSpeech Recognition+1A Survey of Methods, Challenges and Perspectives in Causality
Deep Learning models have shown success in a large variety of tasks by extracting correlation patterns from high-dimensional data but still struggle when generalizing out of their initial distribution. As causal engines …
Deep LearningSurvey