Summary of Can Large Language Models Code Like a Linguist?: a Case Study in Low Resource Sound Law Induction, by Atharva Naik et al.
Can Large Language Models Code Like a Linguist?: A Case Study in Low Resource Sound Law Induction
by Atharva Naik, Kexun Zhang, Nathaniel Robinson, Aravind Mysore, Clayton Marr, Hong Sng Rebecca Byrnes, Anna Cai, Kalvin Chang, David Mortensen
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 is proposed in this paper to cast Sound Law Induction (SLI) as Programming by Examples, leveraging Large Language Models (LLMs) to generate Python sound law programs from sound change examples. The authors evaluate the effectiveness of their method for various LLMs and propose methods to generate synthetic data to fine-tune them for SLI. While LLMs may lag behind existing automated SLI methods, they can complement some of their weaknesses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers called Large Language Models (LLMs) to help with a problem that historical linguists have been doing by hand for a long time. This problem is called Sound Law Induction (SLI). It’s like trying to write a set of rules that can change words from an old language into the same word in a new language. The researchers used these special computers to see if they could make this process easier and less prone to mistakes. |
Keywords
» Artificial intelligence » Synthetic data
