Summary of Let Your Graph Do the Talking: Encoding Structured Data For Llms, by Bryan Perozzi et al.
Let Your Graph Do the Talking: Encoding Structured Data for LLMs
by Bryan Perozzi, Bahare Fatemi, Dustin Zelle, Anton Tsitsulin, Mehran Kazemi, Rami Al-Rfou, Jonathan Halcrow
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI); Machine Learning (stat.ML)
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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 This paper introduces a novel method called GraphToken for encoding structured data into sequential form for use in large language models (LLMs). The proposed approach learns an encoding function that extends prompts with explicit structured information, allowing for significant improvements to graph reasoning tasks. Unlike previous work that focused on limited domains, this effort targets the general encoding of structured data for various reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us figure out how to teach large language models about structures like graphs and networks. The authors created a new way to do this called GraphToken, which makes it easier for the model to understand and work with graph-like information. They tested their approach on some benchmark problems and found that it improved results by up to 73%. This could be useful for lots of different applications where you need to reason about relationships between things. |
