Summary of How to Make the Most Of Llms’ Grammatical Knowledge For Acceptability Judgments, by Yusuke Ide et al.
How to Make the Most of LLMs’ Grammatical Knowledge for Acceptability Judgments
by Yusuke Ide, Yuto Nishida, Justin Vasselli, Miyu Oba, Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe
First submitted to arxiv on: 19 Aug 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 The study proposes new methods to evaluate the grammatical knowledge of large language models (LLMs) using prompts and templates. The traditional approach compares sentence probabilities assigned by LMs, but this may not accurately reflect their grammatical understanding. Instead, the researchers test nine judgment methods in English and Chinese, finding that two techniques – probability readout method “in-template LP” and prompt-based method “Yes/No probability computing” – outperform conventional methods. These methods excel at different linguistic phenomena, indicating they access distinct aspects of LLMs’ knowledge. By ensembling the two methods, accuracy improves further. The study recommends these techniques as more effective alternatives for assessing grammatical knowledge in LMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting better and better at understanding human language. But how do we know if they really understand grammar? One way to test this is by giving them pairs of sentences that are either correct or incorrect, and asking which one is more likely to be true. The problem is that recent language models are trained to perform tasks using prompts, so their raw probability scores might not reflect their actual understanding of grammar. This study tries to fix this issue by developing new methods for evaluating these language models’ grammatical knowledge. They tested nine different ways to do this in both English and Chinese, and found two methods that work really well: one that looks at the probability score directly, and another that asks yes or no questions about whether a sentence is correct. These methods are better than the old way of doing things, and can even be combined to make them even more accurate. |
Keywords
» Artificial intelligence » Probability » Prompt
