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Graph agents extend large language models (LLMs) with the ability to actively explore and reason over knowledge graphs through multi-step interactions with graph tools. However, training capable graph agents typically requires large collections of question-answer pairs and reasoning trajectories, whose manual construct...
This paper proposes a novel model, ContraTGT, which employs a dual-view graph transformer, and puts forth an innovative, learnable data augmentation method, which involves selective masking of elements within dual-view sequences, thereby amplifying the potency of the contrastive learning approach.
GALLM constructs a collaborative graph over text tokens and item tokens, and models three types of relations that are transformed into lightweight learnable attention biases and incorporated into the LLM attention mechanism, enabling collaborative-aware token interactions without introducing an additional graph encoder...
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