Summary of Language Models As Hierarchy Encoders, by Yuan He et al.
Language Models as Hierarchy Encoders
by Yuan He, Zhangdie Yuan, Jiaoyan Chen, Ian Horrocks
First submitted to arxiv on: 21 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 In this paper, researchers tackle a limitation of current language models by introducing a novel approach to explicitly encode hierarchical structures in language. The method, called Hierarchy Transformer (HiT), leverages hyperbolic space to situate the output embedding space within a Poincaré ball with adaptable curvature. This allows for effective clustering and organization of related entities into hierarchies. The authors evaluate HiTs against pre-trained LMs, fine-tuned LMs, and hyperbolic embedding baselines on tasks like transitive inference, subsumption prediction, and knowledge transfer across hierarchies. The results show that HiTs consistently outperform all baselines in these tasks, highlighting the effectiveness of re-training hierarchy encoders. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models can learn hierarchical structures from text data, but this hasn’t been explicitly explored before. Researchers developed a new approach called Hierarchy Transformer (HiT) to help language models better understand language’s hidden hierarchies. HiT uses special math (hyperbolic space) to group similar ideas together and organize them in a logical order. The team tested their new method against existing approaches and found that it worked much better at tasks like understanding relationships between words, predicting what goes with what, and transferring knowledge from one context to another. |
Keywords
* Artificial intelligence * Clustering * Embedding * Embedding space * Inference * Transformer
