Rotary embeddings create bandwidth-dependent attention decay during graph linearization; GaLA corrects this at inference time to boost performance on text-attributed graphs.
Let's ask gnn: Empowering large language model for graph in-context learning
2 Pith papers cite this work. Polarity classification is still indexing.
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GEMS formulates close-ended human-behavior simulation as link prediction on a heterogeneous graph and matches or exceeds LLM performance with three orders of magnitude fewer parameters across three datasets and three evaluation settings.
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Formalizing and Mitigating Structural Distortion in LLM Attention for Graph Reasoning
Rotary embeddings create bandwidth-dependent attention decay during graph linearization; GaLA corrects this at inference time to boost performance on text-attributed graphs.
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Graph-Based Alternatives to LLMs for Human Simulation
GEMS formulates close-ended human-behavior simulation as link prediction on a heterogeneous graph and matches or exceeds LLM performance with three orders of magnitude fewer parameters across three datasets and three evaluation settings.