GTokenLLMs do not fully understand graph tokens, exhibiting over-sensitivity or insensitivity to instruction changes and relying heavily on text for reasoning even when graph information is preserved.
Natural language is all a graph needs.arXiv preprint arXiv:2308.07134, 4(5):7
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PROVSYN synthesizes high-fidelity security provenance graphs via graph generation and LLMs to augment imbalanced datasets, improving downstream APT detection accuracy by up to 38% on benchmarks.
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Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding
GTokenLLMs do not fully understand graph tokens, exhibiting over-sensitivity or insensitivity to instruction changes and relying heavily on text for reasoning even when graph information is preserved.
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No Data? No Problem: Synthesizing Security Graphs for Better Intrusion Detection
PROVSYN synthesizes high-fidelity security provenance graphs via graph generation and LLMs to augment imbalanced datasets, improving downstream APT detection accuracy by up to 38% on benchmarks.