Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multi-View Empowered Structural Graph Wordification for Language Models
Authors: Zipeng Liu, Likang Wu, Ming He, Zhong Guan, Hongke Zhao, Nan Feng
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluations on standard graph tasks demonstrate competitive performance against other state-ofthe-art (SOTA) approaches. Additionally, our framework ensures certain visual interpretability, efficiency, and robustness, marking the promising successful endeavor to achieve token-level alignment between LLMs and GNNs. |
| Researcher Affiliation | Collaboration | 1College of Management and Economics, Tianjin University 2Laboratory of Computation and Analytics of Complex Management Systems (CACMS), Tianjin University 3AI Lab, Lenovo Research |
| Pseudocode | No | The paper describes the methodology using prose and equations, but it does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Code https://github.com/Timothy914/Dr.E |
| Open Datasets | Yes | To evaluate the efficacy of our framework, Dr.E is tested on three benchmark datasets: Cora (Mc Callum et al. 2000), Pub Med (Sen et al. 2008), and OGBN-Arxiv (Hu et al. 2020). |
| Dataset Splits | Yes | We adhere to the dataset splits commonly employed by other methods, such as those detailed in (He et al. 2023). |
| Hardware Specification | Yes | Our experiments are conducted using 2 NVIDIA A800-SXM4-80GB GPUs. |
| Software Dependencies | No | The paper mentions using "Llama2-7B" and "Lo RA PEFT adjustments" but does not provide specific version numbers for these or other software components. |
| Experiment Setup | Yes | We implement Lo RA PEFT adjustments for Llama2-7B and establish two distinct learning rates for the GNN encoder and LLM decoder, set at 1 10 3 and 1 10 4, respectively, with a weight decay of 5 10 4. The hidden dimension for the SAGE convolution is 4096, matching the token embedding of Llama. |