Summary of Science Is Exploration: Computational Frontiers For Conceptual Metaphor Theory, by Rebecca M. M. Hicke et al.
Science is Exploration: Computational Frontiers for Conceptual Metaphor Theory
by Rebecca M. M. Hicke, Ross Deans Kristensen-McLachlan
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| 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 identify and explain conceptual metaphors in natural language data. Conceptual metaphors are systematic mappings between two domains of experience, and their presence can provide evidence for analogical reasoning in human cognition. The authors develop a novel prompting technique using metaphor annotation guidelines and demonstrate that LLMs can accurately detect and describe these metaphors. Furthermore, they show that LLMs can apply procedural guidelines designed for human annotators, revealing their capacity to learn linguistic patterns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at whether computers can understand a special kind of language called conceptual metaphors. These are when we use the same words or ideas from one area to describe something in another area. This is important because it shows how our brains work and how we make connections between things. The researchers created a new way for computers to find these metaphors and showed that they can do it really well. They also found out that computers can follow rules that humans use when identifying these metaphors. |
Keywords
» Artificial intelligence » Prompting
