How the LLM Got Lost in the Network and Discovered Graph Reasoning | by Salvatore Raieli | Sep, 2024

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|GRAPH|LLM|REASONING|GRAPH REASONING|

Enhancing large language models: A journey through graph reasoning and instruction-tuning

LLM and graph reasoning
image created by the author using AI

In a long story format, you have to set a graph for your role. — Sunil Grover

Large Language Models (LLMs) have shown incredible capabilities, and these capabilities have recently been extended beyond the text. On the one hand, we have witnessed multimodal models (e.g., vision-language models); on the other hand, we have witnessed an extension of model capabilities to skills that require reasoning. For example, we now have models dedicated to solving math problems or writing code.

Recently, however, another type of data has captured the attention of researchers. In fact, a great deal of data in the real world can be represented in the form of graphs. For example, social networks are data that are structured as graphs precisely because it is important to represent the relationship between various entities. This is not the only example: in biomedical sciences it is common to represent molecules, and interactions between proteins, as graphs. However, the interaction between LLMs and graphs is recent…

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