Summary of Models Can and Should Embrace the Communicative Nature Of Human-generated Math, by Sasha Boguraev et al.
Models Can and Should Embrace the Communicative Nature of Human-Generated Math
by Sasha Boguraev, Ben Lipkin, Leonie Weissweiler, Kyle Mahowald
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 A novel approach to mathematics is proposed, treating math as situated linguistic communication rather than purely symbolic entities. By recognizing that math data reflects the communicative goals of users, researchers can leverage language models to better understand and generate mathematical expressions. Two case studies demonstrate the benefits of this perspective: language models interpret the equals sign in a humanlike manner, generating different word problems for the same equation depending on its arrangement; and models prefer proofs with naturalistic ordering, despite logical equivalence. This research highlights the potential of AI systems that learn from and represent the communicative intentions underlying human-generated math. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Math can be seen as a language too! A new idea says we should think about math like a conversation between people, not just a set of rules. This way of thinking can help us make better machines that understand and create math problems. Two experiments show how this works: computers can generate different math word problems for the same equation based on how it’s written; and they prefer to see proofs in a logical order, like a story. This research shows AI can learn from and mimic human math, making it more helpful. |
