G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
(2402.07630)Abstract
Given a graph with textual attributes, we enable users to `chat with their graph': that is, to ask questions about the graph using a conversational interface. In response to a user's questions, our method provides textual replies and highlights the relevant parts of the graph. While existing works integrate LLMs and graph neural networks (GNNs) in various ways, they mostly focus on either conventional graph tasks (such as node, edge, and graph classification), or on answering simple graph queries on small or synthetic graphs. In contrast, we develop a flexible question-answering framework targeting real-world textual graphs, applicable to multiple applications including scene graph understanding, common sense reasoning, and knowledge graph reasoning. Toward this goal, we first develop our Graph Question Answering (GraphQA) benchmark with data collected from different tasks. Then, we propose our G-Retriever approach, which integrates the strengths of GNNs, LLMs, and Retrieval-Augmented Generation (RAG), and can be fine-tuned to enhance graph understanding via soft prompting. To resist hallucination and to allow for textual graphs that greatly exceed the LLM's context window size, G-Retriever performs RAG over a graph by formulating this task as a Prize-Collecting Steiner Tree optimization problem. Empirical evaluations show that our method outperforms baselines on textual graph tasks from multiple domains, scales well with larger graph sizes, and resists hallucination. (Our codes and datasets are available at: https://github.com/XiaoxinHe/G-Retriever.)
Overview
-
The article presents 'G-Retriever', a novel framework that combines Graph Neural Networks, LLMs, and Retrieval-Augmented Generation to enhance question-answering capabilities over textual graphs.
-
The GraphQA benchmark is introduced, featuring datasets like ExplaGraphs, SceneGraphs, and WebQSP, to evaluate the model's performance on various graph-related question-answering tasks.
-
Empirical evaluation demonstrates that G-Retriever outperforms state-of-the-art models while reducing inefficiencies and hallucinations in generating factually correct answers, providing robust solutions for complex, large-scale textual graphs.
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
The article introduces "G-Retriever," a novel framework designed for retrieval-augmented generation (RAG) to enhance the understanding and question-answering (QA) capabilities over textual graphs. With the integration of Graph Neural Networks (GNNs) and LLMs, the proposed approach aims to enable a conversational interface through which users can seamlessly interact with and inquire about complex real-world textual graphs.
Key Contributions
GraphQA Benchmark The study addresses a significant gap in QA benchmarks tailored to graph modalities by presenting the GraphQA benchmark. This benchmark encompasses a diverse set of datasets: ExplaGraphs for commonsense reasoning, SceneGraphs for visual question answering, and WebQSP for knowledge graph-based multi-hop question answering. The standardization and processing of these datasets into the GraphQA format allow a comprehensive evaluation of models in answering a wide array of questions related to real-world graph applications.
G-Retriever Architecture G-Retriever is built upon the synergy of GNNs, LLMs, and RAG, fine-tuned to provide a robust QA framework that scales to larger textual graphs and resists hallucinations.
- Indexing: The approach begins with encoding node and edge attributes using a pre-trained LM (SentenceBert), creating embeddings stored in a nearest-neighbor data structure for efficient query processing.
- Retrieval: Utilizing cosine similarity, the system retrieves semantically relevant nodes and edges from the graph, conditioned on the query.
- Subgraph Construction: The retrieval task is cast as a Prize-Collecting Steiner Tree (PCST) optimization problem to construct an optimally connected subgraph, encompassing relevant nodes and edges while controlling for manageable graph size.
- Answer Generation: A GAT-based graph encoder models the retrieved subgraph. The output is projected into the LLM's vector space and combined with the query and a textualized form of the graph for final answer generation through a frozen LLM, augmented via soft prompting.
Experimental Evaluation
The empirical evaluation affirms G-Retriever's superior performance across the datasets in the GraphQA benchmark.
Main Results The method outperforms baseline and state-of-the-art models in various configurations (Inference-Only, Frozen LLM + Prompt Tuning, and Tuned LLM):
- On ExplaGraphs, SceneGraphs, and WebQSP datasets, G-Retriever achieved improvements (e.g., 47.99% in ExplaGraphs under prompt tuning) relative to baseline models.
- The integration of RAG and graph-specific optimizations significantly enhanced model efficiency, reducing the average number of tokens and nodes processed by up to 99% on larger graphs like those in the WebQSP dataset.
- G-Retriever demonstrated a substantial reduction in hallucinations, confirming its effectiveness in generating factually consistent answers by directly retrieving accurate graph information.
Ablation Study The study illustrates the contributions of each component in G-Retriever, revealing that omitting crucial elements, such as the graph encoder or textualized graph, results in considerable performance drops.
Implications and Future Work
Practical Implications G-Retriever’s capability to handle complex and large-scale textual graphs extends its applicability to diverse fields including knowledge management, e-commerce, and scene analysis, thus being adaptable to numerous real-world applications that involve intricate graph-structured data.
Theoretical Implications The incorporation of RAG into the graph domain underscores the effectiveness of retrieval-based approaches beyond conventional language tasks, presenting a transformative strategy that mitigates hallucination issues prevalent in both text and graph-based models.
Future Developments Future work could delve into dynamic, trainable retrieval mechanisms within the RAG framework, potentially further optimizing the retrieval and generation process. Enhanced retrieval strategies may facilitate a more flexible and adaptive retrieval schema, catering to an increasingly broader array of graph-related tasks.
Conclusion
The presented work signifies a forward step in graph-based QA by blending graph-neural and language models, emphasizing the feasibility and advantages of the RAG approach in large and complex textual graph settings. G-Retriever's design and empirical success highlight its potential for advancing both human-computer interaction and automated understanding within graph-related AI applications.
Create an account to read this summary for free: