Summary of Hyperbolic Fine-tuning For Large Language Models, by Menglin Yang et al.
Hyperbolic Fine-tuning for Large Language Models
by Menglin Yang, Aosong Feng, Bo Xiong, Jihong Liu, Irwin King, Rex Ying
First submitted to arxiv on: 5 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Neural and Evolutionary Computing (cs.NE)
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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 investigates whether large language models (LLMs) can benefit from being fine-tuned in non-Euclidean spaces. Research reveals that token frequencies follow a power-law distribution, with high-frequency tokens clustering near the origin and low-frequency tokens positioned farther away. Token embeddings also exhibit hyperbolicity, indicating a latent tree-like structure. Building on these findings, the study proposes a new method called HypLoRA for fine-tuning LLMs in hyperbolic space to better exploit complex structures. The authors demonstrate that HypLoRA enhances performance on reasoning tasks, particularly for complex problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how large language models work and whether they can be made even better. They found some strange things about the way words are connected in these models, like a pattern where common words are close together and rare words are far apart. They also discovered that these connections have a special shape, like a tree. The researchers came up with a new way to make these language models even smarter by working with this special shape. This made them better at solving tricky problems. |
Keywords
» Artificial intelligence » Clustering » Fine tuning » Token
