How to make powerful LLMs understand graphs and their structure? 🕸️ With Graph Language Models! They take a pre-trained language model and fit it with the ability to process graphs. Watch if you're curious about how this works (hint: choose the right positional embeddings)!
📃 Moritz Plenz and Anette Frank. 2024. Graph Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Bangkok, Thailand, ACL 2024 https://aclanthology.org/2024.acl-long.245/
💻 Code for Graph Language Models: https://github.com/Heidelberg-NLP/GraphLanguageModels
Follow Moritz Plenz (first author) on:
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Outline:
00:00 LLM for graphs
01:08 Motivation
02:02 Key idea of Graph LLMs
02:25 Relative Positional Encodings
03:00 Method (Graph LLMs)
04:04 Experiments and Evaluation
04:49 Results
06:07 Outro
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Video editing: Nils Trost
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