Large Language Models (LLMs) like GPT, BERT, PaLM, and LLaMA have advanced Natural Language Processing and Generation. They excel at various tasks, but there’s growing interest in their application to graph-based tasks. Research explores integrating LLMs with graphs, proposing classification of scenarios, evaluating models, curating materials, and suggesting future research directions. Read more at the Paper.
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The Advancements of Large Language Models (LLMs) in Natural Language Processing (NLP) and Natural Language Generation (NLG)
The well-known Large Language Models (LLMs) like GPT, BERT, PaLM, and LLaMA have shown remarkable performance in tasks such as question answering, content generation, and text summarization. These models have been pre-trained on large text corpora and have brought great advancements in Natural Language Processing (NLP) and Natural Language Generation (NLG).
Integration of LLMs with Graphs
While LLMs have excelled in handling plain text, the need for handling applications where textual data is linked to structural information in the form of graphs is growing. Researchers have been exploring how LLMs, with their strong text-based reasoning, can be applied to basic graph reasoning tasks, including matching subgraphs, shortest paths, and connection inference. The integration of LLMs with graphs is associated with three types of graph-based applications: pure graphs, text-rich graphs, and text-paired graphs. Techniques include treating LLMs as task predictors, feature encoders for Graph Neural Networks (GNNs), or aligners with GNNs, depending on their function and interaction with GNNs.
Practical Applications and Future Research
LLMs are gaining popularity for graph-based applications, and recent research has provided a methodical overview of the integration of language models with graphs. The research has emphasized the practical applications of these techniques, shared benchmark datasets, and open-source scripts, and outlined possible future study topics in this rapidly developing field. The team has also suggested six possible directions for further research in the field of language models on graphs.
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