Data Skeptic
GraphText
Oct 31, 2023
Jianan Zhao, a computer science student, joins to discuss using graphs with LLMs efficiently. They explore graph inductive bias, graph machine learning, limitations of natural language models for graphs, graph text as a preprocessing step, information loss in translation process, and comparison with graph neural networks.
30:57
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Quick takeaways
- Graph inductive bias plays a crucial role in transferring knowledge between domains in graph-related problems.
- Graph text framework allows large language models to process and reason about graphs by converting graph data into a tree structure.
Deep dives
Graphs as a new frontier for large language models
Large language models like ChatGPT are expanding their capabilities beyond language tasks and are being explored for their potential in solving graph-related problems. Graphs, consisting of nodes and edges, are used in various domains such as social network analysis. The challenge lies in converting graph data into a format that can be processed by large language models. Graph inductive bias, which allows useful information to be learned from one graph and applied to another, plays a crucial role. The distinction between homophilic and heterophilic graphs, where nodes tend to connect with similar or dissimilar nodes, further influences the transferability of knowledge between domains.
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