Summary of On Eliciting Syntax From Language Models Via Hashing, by Yiran Wang et al.
On Eliciting Syntax from Language Models via Hashing
by Yiran Wang, Masao Utiyama
First submitted to arxiv on: 5 Oct 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 The paper explores the potential of leveraging binary representation for unsupervised parsing, also known as grammar induction. The authors upgrade the bit-level CKY algorithm to encode lexicon and syntax in a unified binary representation space, switch training from supervised to unsupervised under the contrastive hashing framework, and introduce a novel loss function to impose stronger yet balanced alignment signals. The model achieves competitive performance on various datasets, demonstrating its effectiveness and efficiency for acquiring high-quality parsing trees from pre-trained language models at a low cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to get computers to understand grammar without being taught. It uses special code called binary representation that’s good at keeping information. The researchers try using this code to teach computers the rules of grammar, just by showing them lots of text. They make some changes to an old algorithm and a new way of training to help it work better. The results are pretty good on different tests, so they think their method is helpful for getting computers to understand grammar from language models. |
Keywords
» Artificial intelligence » Alignment » Loss function » Parsing » Supervised » Syntax » Unsupervised
