Summary of Mission: Impossible Language Models, by Julie Kallini et al.
Mission: Impossible Language Models
by Julie Kallini, Isabel Papadimitriou, Richard Futrell, Kyle Mahowald, Christopher Potts
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 challenges the idea that large language models (LLMs) like GPT-2 can learn languages that are impossible for humans to learn, a claim made by linguist Chomsky. The authors created synthetic languages of varying complexity, including some that are inherently impossible and others that might not be intuitive but are considered so in linguistics. They then tested the ability of GPT-2 small models to learn these languages at different stages throughout training. The results show that GPT-2 struggles to learn impossible languages compared to English as a control, which challenges the core claim. This study aims to open up new avenues for using LLMs in cognitive and typological investigations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper questions whether large language models (LLMs) can learn languages that are too hard for humans to learn. To test this idea, the researchers created some fake languages that are impossible or very hard for humans to learn. They then used a small language model called GPT-2 to try and learn these languages. The results show that GPT-2 had trouble learning the impossible languages. This study helps us understand how LLMs can be used to study how we think and communicate. |
Keywords
* Artificial intelligence * Gpt * Language model
