Summary of Llasa: Large Language and Structured Data Assistant, by Yao Xu et al.
LLaSA: Large Language and Structured Data Assistant
by Yao Xu, Shizhu He, Jiabei Chen, Zeng Xiangrong, Bingning Wang, Guang Liu, Jun Zhao, Kang Liu
First submitted to arxiv on: 16 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research paper proposes a framework called LLaSA (Large Language and Structured Data Assistant) that enhances the ability of Large Language Models (LLMs) to handle structured data. The authors draw inspiration from Vision-Language Models and introduce Graph Neutral Networks (GNNs) as an additional modality into the input of LLMs to improve their performance on Structured Knowledge Grounding (SKG) tasks. However, existing GNN-enhanced LLMs have limitations in uniformly processing various forms of structured data. To address this issue, the authors propose a unified hypergraph format for representing different types of structured data and use self-supervised learning to pretrain a hypergraph encoder. This framework, combined with a G-Former compressing encoded hypergraph representations with cross-attention, can adapt to various LLMs and enhance their ability to process different types of structured data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper helps computers better understand tables, graphs, and databases by creating a new way for them to work together. The authors took inspiration from how humans learn and created a special kind of network that combines language models with graph networks. This lets the computer process different kinds of structured data in a more uniform way. The authors tested their approach on several tasks and found that it works better than previous methods. |
Keywords
» Artificial intelligence » Cross attention » Encoder » Gnn » Grounding » Self supervised
