Defining Knowledge: Bridging Epistemology and Large Language Models
Constanza Fierro, Ruchira Dhar, Filippos Stamatiou, Nicolas Garneau, Anders Søgaard
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.
BibTeX
@inproceedings{fierro-etal-2024-defining,
title = "Defining Knowledge: Bridging Epistemology and Large Language Models",
author = "Fierro, Constanza and
Dhar, Ruchira and
Stamatiou, Filippos and
Garneau, Nicolas and
S{\o}gaard, Anders",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.900/",
doi = "10.18653/v1/2024.emnlp-main.900",
pages = "16096--16111"
}