Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning (2305.19523v5)

Abstract: Representation learning on text-attributed graphs (TAGs) has become a critical research problem in recent years. A typical example of a TAG is a paper citation graph, where the text of each paper serves as node attributes. Initial graph neural network (GNN) pipelines handled these text attributes by transforming them into shallow or hand-crafted features, such as skip-gram or bag-of-words features. Recent efforts have focused on enhancing these pipelines with LLMs (LMs), which typically demand intricate designs and substantial computational resources. With the advent of powerful LLMs such as GPT or Llama2, which demonstrate an ability to reason and to utilize general knowledge, there is a growing need for techniques which combine the textual modelling abilities of LLMs with the structural learning capabilities of GNNs. Hence, in this work, we focus on leveraging LLMs to capture textual information as features, which can be used to boost GNN performance on downstream tasks. A key innovation is our use of explanations as features: we prompt an LLM to perform zero-shot classification, request textual explanations for its decision-making process, and design an LLM-to-LM interpreter to translate these explanations into informative features for downstream GNNs. Our experiments demonstrate that our method achieves state-of-the-art results on well-established TAG datasets, including Cora, PubMed, ogbn-arxiv, as well as our newly introduced dataset, tape-arxiv23. Furthermore, our method significantly speeds up training, achieving a 2.88 times improvement over the closest baseline on ogbn-arxiv. Lastly, we believe the versatility of the proposed method extends beyond TAGs and holds the potential to enhance other tasks involving graph-text data. Our codes and datasets are available at: https://github.com/XiaoxinHe/TAPE.

Comprehensive Analysis of "Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning"

In "Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning," the authors present a novel approach to augment representation learning in text-attributed graphs (TAGs) by integrating LLMs into the graph neural network (GNN) pipeline. This paper addresses the growing need for techniques that effectively combine the textual modeling capabilities of LLMs with the structural learning prowess of GNNs.

Core Contribution

The paper introduces a framework that leverages LLMs to capture textual information in TAGs and transforms them into features to enhance GNN performance on downstream tasks. A key innovation is the use of LLM-extracted explanations as features. By utilizing LLMs such as GPT to perform zero-shot classifications and requesting textual explanations for these decisions, the authors propose an LLM-to-LM interpreter designed to translate these explanations into valuable features for subsequent GNN processing.

Experimental Strength and Results

The authors evaluate their framework across well-established TAG datasets like Cora, PubMed, ogbn-arxiv, and the newly introduced tape-arxiv23 dataset. Their method achieves state-of-the-art results, demonstrating significant improvements in accuracy and efficiency. For example, the framework achieves a 73.5% accuracy on the ogbn-arxiv dataset using GPT-3.5 for zero-shot classification, a notable feat when compared to the SOTA model's accuracy of 76.6%. Their approach also improves training efficiency, achieving a 2.88 times speed-up over the closest baseline on ogbn-arxiv.

Theoretical Implications and Practical Applications

The method's versatility is a central theme, with implications extending beyond TAG tasks such as text classification, recommendation systems, and social networks. The process of transforming textual explanations from LLMs into AGN-compatible node representations underscores the innovative use of LLM outputs in static graph tasks. Moreover, the proposed framework addresses scalability concerns with LLMs, allowing efficient usage via LLMing as a service (LMaaS), thus bypassing the computational challenges often associated with LLM inference.

Future Prospects

Looking ahead, automated prompt generation for LLM-to-LM tasks and further exploration of LLM scalability are promising avenues for refinement. This paper opens new pathways by utilizing LLM explanations—not just raw textual outputs—potentially applicable to evolving tasks in dynamic and large-scale TAGs.

Conclusion

The work significantly advances representation learning for TAGs by merging cutting-edge LLM capabilities with the structural learning strengths of GNNs, establishing a robust baseline for future research endeavors. The approach provides a noteworthy enhancement to modeling TAGs, streamlining complex reasoning into practical feature sets for GNNs. As such, the methodology stands as a crucial contribution to the field, promising to influence future methodologies in integrating language-processing models with graph-based learning systems.