Despite the unprecedented success of applying Large Language Models (LLMs) to graph discriminative tasks such as node classification and link prediction, its potential for graph structure generation remains largely unexplored. To fill this crucial gap, this paper presents a systematic investigation into the capability of LLMs for graph structure generation. Specifically, we design prompts triggering LLMs to generate codes that optimize network properties by injecting domain expertise from network science. Since graphs in different domains exhibit unique structural properties captured by various metrics (e.g., clustering coefficient capturing triangles in social networks while squares reflecting road segments in transportation networks), we first evaluate the capability of LLMs to generate graphs satisfying each structural property in different domains. After that, we select the optimal property configurations and benchmark the graph structure generation performance of LLMs against established graph generative models across multiple domains. Our findings shed light on generating graph structures from an LLM perspective. Our code is publically available https://github.com/yuwvandy/LLM-GraphGen.

Fine-tuning provides an effective means to specialize pre-trained models for various downstream tasks. However, fine-tuning often incurs high memory overhead, especially for large transformer-based models, such as LLMs. While existing methods may reduce certain parts of the memory required for fine-tuning, they still require caching all intermediate activations computed in the forward pass to update weights during the backward pass. In this work, we develop TokenTune, a method to reduce memory usage, specifically the memory to store intermediate activations, in the fine-tuning of transformer-based models. During the backward pass, TokenTune approximates the gradient computation by backpropagating through just a subset of input tokens. Thus, with TokenTune, only a subset of intermediate activations are cached during the forward pass. Also, TokenTune can be easily combined with existing methods like LoRA, further reducing the memory cost. We evaluate our approach on pre-trained transformer models with up to billions of parameters, considering the performance on multiple downstream tasks such as text classification and question answering in a few-shot learning setup. Overall, TokenTune achieves performance on par with full fine-tuning or representative memory-efficient fine-tuning methods, while greatly reducing the memory footprint, especially when combined with other methods with complementary memory reduction mechanisms. We hope that our approach will facilitate the fine-tuning of large transformers, in specializing them for specific domains or co-training them with other neural components from a larger system. Our code is available at https://github.com/facebookresearch/tokentune.