# Large Language Models as a Tool for Mining Object Knowledge

arXiv: https://arxiv.org/abs/2410.12959 · HTML: https://mlsanity.com/paper/2410.12959/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Builds on (most cited first, 14 of 14)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020)
- [Boosting Theory-of-Mind Performance in Large Language Models via Prompting](https://mlsanity.com/paper/2304.11490/) (2023)
- [TransOMCS: From Linguistic Graphs to Commonsense Knowledge](https://mlsanity.com/paper/2005.00206/) (2020)
- [Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT](https://mlsanity.com/paper/2210.04186/) (2022)
- [Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge](https://mlsanity.com/paper/2101.06013/) (2021)
- [Refined Commonsense Knowledge from Large-Scale Web Contents](https://mlsanity.com/paper/2112.04596/) (2021)
- [Do language models have coherent mental models of everyday things?](https://mlsanity.com/paper/2212.10029/) (2022)
- [Semantic features of object concepts generated with GPT-3](https://mlsanity.com/paper/2202.03753/) (2022)
- [VisionKG: Unleashing the Power of Visual Datasets via Knowledge Graph](https://mlsanity.com/paper/2309.13610/) (2023)
- [Find The Gap: Knowledge Base Reasoning For Visual Question Answering](https://mlsanity.com/paper/2404.10226/) (2024)
- [Symbolic Learning and Reasoning with Noisy Data for Probabilistic Anchoring](https://mlsanity.com/paper/2002.10373/) (2020)

## Abstract (arXiv)

Commonsense knowledge is essential for machines to reason about the world. Large language models (LLMs) have demonstrated their ability to perform almost human-like text generation. Despite this success, they fall short as trustworthy intelligent systems, due to the opacity of the basis for their answers and a tendency to confabulate facts when questioned about obscure entities or technical domains. We hypothesize, however, that their general knowledge about objects in the everyday world is largely sound. Based on that hypothesis, this paper investigates LLMs' ability to formulate explicit knowledge about common physical artifacts, focusing on their parts and materials. Our work distinguishes between the substances that comprise an entire object and those that constitute its partsunicodex2014a previously underexplored distinction in knowledge base construction. Using few-shot with five in-context examples and zero-shot multi-step prompting, we produce a repository of data on the parts and materials of about 2,300 objects and their subtypes. Our evaluation demonstrates LLMs' coverage and soundness in extracting knowledge. This contribution to knowledge mining should prove useful to AI research on reasoning about object structure and composition and serve as an explicit knowledge source (analogous to knowledge graphs) for LLMs performing multi-hop question answering.