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 large language models (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:
this https URL)

给定具有文本属性的图，我们使用对话界面使用户能够与其图进行交流，在回答用户问题时提供文本回复并突出显示图的相关部分。通过开发我们的图问题回答（GraphQA）基准和集成 GNN、LLM 和 RAG 的 G-Retriever 方法，我们在多个领域的文本图任务中超越基准，而且可以适应更大的图大小并抗幻觉。

G-Retriever：文本图理解与问答的检索增强生成

G-Retriever: Retrieval-Augmented Generation for Textual Graph  Understanding and Question Answering

Building a deep learning model for a Question-Answering (QA) task requires a
lot of human effort, it may need several months to carefully tune various model
architectures and find a best one. It's even harder to find different excellent
models for multiple datasets. Recent works show that the best model structure
is related to the dataset used, and one single model cannot adapt to all tasks.
In this paper, we propose an automated Question-Answering framework, which
could automatically adjust network architecture for multiple datasets. Our
framework is based on an innovative evolution algorithm, which is stable and
suitable for multiple dataset scenario. The evolution algorithm for search
combine prior knowledge into initial population and use a performance estimator
to avoid inefficient mutation by predicting the performance of candidate model
architecture. The prior knowledge used in initial population could improve the
final result of the evolution algorithm. The performance estimator could
quickly filter out models with bad performance in population as the number of
trials increases, to speed up the convergence. Our framework achieves 78.9 EM
and 86.1 F1 on SQuAD 1.1, 69.9 EM and 72.5 F1 on SQuAD 2.0. On NewsQA dataset,
the found model achieves 47.0 EM and 62.9 F1.

本文提出了一种基于创新的进化算法的自动问答框架，适用于多个数据集，并能自动调整网络架构，从而找到最佳解。在 SQuAD 1.1 和 SQuAD 2.0 数据集上，我们的框架实现了 78.9 EM 和 86.1 F1 以及 69.9 EM 和 72.5 F1 的效果，在 NewsQA 数据集上，实现了 47.0 EM 和 62.9 F1 的效果。