Defining Knowledge: Bridging Epistemology and Large Language Models

• URL: http://arxiv.org/abs/2410.02499v1
• Date: Thu, 3 Oct 2024 14:01:01 GMT
• Title: Defining Knowledge: Bridging Epistemology and Large Language Models
• Authors: Constanza Fierro, Ruchira Dhar, Filippos Stamatiou, Nicolas Garneau, Anders Søgaard,
• Abstract summary: We review standard definitions of knowledge in NLP and we formalize interpretations applicable to large language models (LLMs) We conduct a survey of 100 professional philosophers and computer scientists to compare their preferences in knowledge definitions and their views on whether LLMs can really be said to know.
• Score: 37.41866724160848
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge claims are abundant in the literature on large language models (LLMs); but can we say that GPT-4 truly "knows" the Earth is round? To address this question, we review standard definitions of knowledge in epistemology and we formalize interpretations applicable to LLMs. In doing so, we identify inconsistencies and gaps in how current NLP research conceptualizes knowledge with respect to epistemological frameworks. Additionally, we conduct a survey of 100 professional philosophers and computer scientists to compare their preferences in knowledge definitions and their views on whether LLMs can really be said to know. Finally, we suggest evaluation protocols for testing knowledge in accordance to the most relevant definitions.

Related papers

• 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)
• Towards Reliable Latent Knowledge Estimation in LLMs: In-Context Learning vs. Prompting Based Factual Knowledge Extraction [15.534647327246239]
  We propose an approach for estimating the latent knowledge embedded inside large language models (LLMs) We leverage the in-context learning abilities of LLMs to estimate the extent to which an LLM knows the facts stored in a knowledge base.
  arXiv  Detail & Related papers  (2024-04-19T15:40:39Z)
• Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models [51.72963030032491]
  Knowledge documents for large language models (LLMs) may conflict with the memory of LLMs due to outdated or incorrect knowledge. We construct a new dataset, dubbed KNOT, for knowledge conflict resolution examination in the form of question answering.
  arXiv  Detail & Related papers  (2024-04-04T16:40:11Z)
• Large Knowledge Model: Perspectives and Challenges [37.42721596964844]
  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)
  arXiv  Detail & Related papers  (2023-12-05T12:07:30Z)
• Do PLMs Know and Understand Ontological Knowledge? [72.48752398867651]
  Ontological knowledge comprises classes and properties and their relationships. It is significant to explore whether Pretrained Language Models (PLMs) know and understand such knowledge. Our results show that PLMs can memorize certain ontological knowledge and utilize implicit knowledge in reasoning.
  arXiv  Detail & Related papers  (2023-09-12T03:20:50Z)
• 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.