In recent years, large language models have achieved state-of-the-art performance across multiple domains. However, the progress in the field of graph reasoning with LLM remains limited. Our work delves into this gap by thoroughly investigating graph reasoning with LLMs. In this work, we reveal the impact of the order of graph description on LLMs' graph reasoning performance, which significantly affects LLMs' reasoning abilities. By altering this order, we enhance the performance of LLMs from 42.22\% to 70\%. Furthermore, we introduce the Scaled Graph Reasoning benchmark for assessing LLMs' performance across various graph sizes and evaluate the relationship between LLMs' graph reasoning abilities and graph size. We discover that the graph reasoning performance of LLMs does not monotonically decrease with the increase in graph size. The experiments span several mainstream models, including GPT-3.5, LLaMA-2-7B, and LLaMA-2-13B, to offer a comprehensive evaluation.
In recent years, Large Language Models have reached state-of-the-art performance across multiple domains. However, the progress in the field of graph reasoning remains limited. Our work delves into this gap by thoroughly investigating graph reasoning with LLM. In this work, we reveal the impact of text sequence on LLM spatial understanding, finding that graph-descriptive text sequences significantly affect LLM reasoning performance on graphs. By altering the graph-descriptive text sequences, we enhance the performance of LLM from 42.22\% to 70\%. Furthermore, we evaluate the relationship between LLM performance and graph size, discovering that the reasoning performance of LLM does not monotonically decrease with the increase in graph size. Conclusively, we introduce the Scaled Graph Reasoning benchmark for assessing LLM performance across varied graph sizes.
Exploring the application of large language models (LLMs) to graph learning is a emerging endeavor. However, the vast amount of information inherent in large graphs poses significant challenges to this process. This work focuses on the link prediction task and introduces $\textbf{LPNL}$ (Link Prediction via Natural Language), a framework based on large language models designed for scalable link prediction on large-scale heterogeneous graphs. We design novel prompts for link prediction that articulate graph details in natural language. We propose a two-stage sampling pipeline to extract crucial information from the graphs, and a divide-and-conquer strategy to control the input tokens within predefined limits, addressing the challenge of overwhelming information. We fine-tune a T5 model based on our self-supervised learning designed for link prediction. Extensive experimental results demonstrate that LPNL outperforms multiple advanced baselines in link prediction tasks on large-scale graphs.
The dynamic nature of language, particularly evident in the realm of slang and memes on the Internet, poses serious challenges to the adaptability of large language models (LLMs). Traditionally anchored to static datasets, these models often struggle to keep up with the rapid linguistic evolution characteristic of online communities. This research aims to bridge this gap by enhancing LLMs' comprehension of the evolving new concepts on the Internet, without the high cost of continual retraining. In pursuit of this goal, we propose a new benchmark $\textbf{SLANG}$, which can autonomously integrates novel data to stay dataset up-to-date, to assess LLMs' capability in comprehending emerging concepts and an approach $\textbf{FOCUS}$, which uses causal inference to enhance LLMs to understand new phrases and their colloquial context. Our benchmark and approach involves digesting real-world instances of linguistic shifts, serving as contextual beacons, to form more precise and contextually relevant connections between newly emerging expressions and their meanings. The empirical analysis shows that our causal inference-based approach outperforms the traditional models in terms of precision and relevance in the comprehension of Internet slang and memes.
Exploring the application of large-scale language models to graph learning is a novel endeavor. However, the vast amount of information inherent in large graphs poses significant challenges to this process. This paper focuses on the link prediction task and introduces LPNL (Link Prediction via Natural Language), a framework based on a large language model designed for scalable link prediction on large-scale heterogeneous graphs.We design novel prompts for link prediction that articulate graph details in natural language. We propose a two-stage sampling pipeline to extract crucial information from large-scale heterogeneous graphs, and a divide-and-conquer strategy to control the input token count within predefined limits, addressing the challenge of overwhelming information. We fine-tune a T5 model based on our self-supervised learning designed for for link prediction. Extensive experiments on a large public heterogeneous graphs demonstrate that LPNL outperforms various advanced baselines, highlighting its remarkable performance in link prediction tasks on large-scale graphs.
In unsupervised learning, variational auto-encoders (VAEs) are an influential class of deep generative models with rich representational power of neural networks and Bayesian methods. However, VAEs suffer from assigning higher likelihood to out-of-distribution (OOD) inputs than in-distribution (ID) inputs. Recent studies advise that the deep generative models with reliable uncertainty estimation is critical to a deep understanding of OOD inputs. Meanwhile, noise contrastive prior (NCP) is an emerging promising method for obtaining uncertainty, with the advantages of easy to scale, being trainable, and compatibility with extensive models. Inspired by these ideas, We propose an improved noise contrastive prior (INCP) to acquire reliable uncertainty estimate for standard VAEs. By combining INCP with the encoder of VAE, patterns between OOD and ID inputs can be well captured and distinguished. Our method outperforms standard VAEs on the FashionMNIST and CIFAR10 datasets. We also demonstrate the preferred robustness of our model by the extensive experiments on anomaly detection tasks.