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Found 5 papers, 3 papers with code
A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models
Large language models (LLMs) have demonstrated remarkable capabilities in a wide range of tasks, yet their application to specialized domains remains challenging due to the need for deep expertise.
Each Graph is a New Language: Graph Learning with LLMs
By treating graphs as a new language, GDL4LLM enables LLMs to model graph structures adequately and concisely for node classification tasks.
Modality-Aware Integration with Large Language Models for Knowledge-based Visual Question Answering
Specifically, (i) we propose a two-stage prompting strategy with LLMs to densely embody the image into a scene graph with detailed visual features; (ii) We construct a coupled concept graph by linking the mentioned entities with external facts.
Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendations
To evaluate our approach, we introduce a cold-start recommendation benchmark, and the results demonstrate that the enhanced small language models can achieve comparable cold-start recommendation performance to that of large models with only $17\%$ of the inference time.
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation
In this paper, we base on the phenomenon and propose a general and novel research problem of generalized graph anomaly detection that aims to effectively identify anomalies on both the training-domain graph and unseen testing graph to eliminate potential dangers.
