LLaTA: Unlocking Graph Structure Learning with Tree-Guided Large Language Models

Name: LLaTA: Unlocking Graph Structure Learning with Tree-Guided Large Language Models
Uploaded: 2026-10-19T10:32:22.004347+00:00
Duration: 264 s
Description: LLaTA reformulates graph structure learning (GSL) by treating it as a tree optimization problem instead of training edge predictors. It uses structural entropy to build an encoding tree that guides a large language model's in-context learning to integrate textual information. This allows for training-free, decoupled GSL that achieves state-of-the-art results without fine-tuning the LLM.

LLaTA reformulates graph structure learning (GSL) by treating it as a tree optimization problem instead of training edge predictors. It uses structural entropy to build an encoding tree that guides a large language model's in-context learning to integrate textual information. This allows for training-free, decoupled GSL that achieves state-of-the-art results without fine-tuning the LLM.

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