Summary of Reasoning About Concepts with Llms: Inconsistencies Abound, by Rosario Uceda-sosa et al.
Reasoning about concepts with LLMs: Inconsistencies abound
by Rosario Uceda-Sosa, Karthikeyan Natesan Ramamurthy, Maria Chang, Moninder Singh
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the ability of large language models (LLMs) to consistently and systematically organize knowledge into abstract concepts. The authors demonstrate that when questioned methodically, LLMs often display significant inconsistencies in their understanding of a given domain. They propose using knowledge graphs (KGs) or ontologies as a framework for representing conceptualizations of domains, which can be used to reveal these inconsistencies across multiple LLMs. Additionally, the paper suggests strategies for domain experts to evaluate and improve the coverage of key concepts in LLMs of varying sizes. The authors successfully enhance the performance of LLMs using simple KG-based prompting strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well language models can summarize and organize information into general ideas. Researchers found that when they asked these models questions, many of them gave different answers for the same topic. They used a special kind of map called a knowledge graph to show how concepts are related and discovered that even small maps could reveal where these models got things wrong. The authors also share ways experts can check if language models are missing important information or getting it wrong. By using this approach, they were able to make some language models better at answering questions. |
Keywords
» Artificial intelligence » Knowledge graph » Prompting
