Summary of Language Models Align with Human Judgments on Key Grammatical Constructions, by Jennifer Hu et al.
Language models align with human judgments on key grammatical constructions
by Jennifer Hu, Kyle Mahowald, Gary Lupyan, Anna Ivanova, Roger Levy
First submitted to arxiv on: 19 Jan 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 LLMs prompt large language models to elicit grammaticality judgments on 80 English sentences, revealing a “yes-response bias” and a failure to distinguish between grammatical and ungrammatical sentences. However, re-evaluating the performance using established practices shows that LLMs capture human linguistic behaviors remarkably well, achieving high accuracy overall and fine-grained variation in human judgments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are great at understanding language! They can tell if a sentence is grammatically correct or not. But do they make mistakes like humans do? Some researchers thought they did, but after re-checking, it seems LLMs actually capture how people think about language very well. |
Keywords
» Artificial intelligence » Prompt
