G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
Authors: Xiaoxin He
Yijun Tian
Yifei Sun
Nitesh V Chawla
Thomas Laurent
Yann LeCun
Xavier Bresson
Bryan Hooi
Venue: NeurIPS 2024 poster (2024)
Keywords: Retrieval Augmented Generation
Graph Question Answering
Graph Neural Network
Large Language Model
Textual Graphs
Primary area: graph_neural_networks

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 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 a Graph Question Answering (GraphQA) benchmark with data collected from different tasks. Then, we propose our \textit{G-Retriever} method, introducing the first retrieval-augmented generation (RAG) approach for general textual graphs, which 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, \textit{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 mitigates hallucination.~\footnote{Our codes and datasets are available at: \url{https://github.com/XiaoxinHe/G-Retriever}}

G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
Xiaoxin He
Yijun Tian
Yifei Sun
Nitesh V. Chawla²
Thomas Laurent
Yann LeCun
Xavier Bresson' Bryan Hooi
(xiaoxin, yifeisun, zaviercs, bhooi www.amomo.uuu............u.u mus edu.sg
(yijun. tian, nchawla) edu, tlaurent@lmu edu, yann nyu. edu
National University of Singapore Universityy of Notre Dame "New York University 3Meta AI
LLoyola Marymount University
Abstract
Given a graph with textual attributes, we enable users to 'chat with their graph': that to ask questions about the graph using conversational interface. In response 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 an- swering simple graph queries small or synthetic graphs. In contrast, we develop flexible question-answer answering framework targeting real-world textual graphs, appli- cable to multiple applications including scene graph understanding, common sense reasoning, and knowledge graph reasoning. Toward this goal. we first develop a Graph Question Answering (GraphQA) benchmark with data collected from differ- ent tasks. Then, we propose our G-Retriever method, introducing the first retrieval- augmented generation (RAG) approach for general textual graphs, which can be fine-tuned to enhance graph understanding via soft prompting. To resist hallucina- tion 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 mitigates hallucination. Our codes and datasets are available at: hhtps:: /ttess:/gein //tp://gttttbbbtt com/
Introduction
Graphs and Large Language Models (LLMs). The advent LLMs has significantly shaped the artificial intelligence landscape. As these models applied increasingly diverse tasks, their ability to process complex structured data will be increasingly vital. In particular, our interconnected world, significant portion of real-world data inherently possesses graph structure, such as the Web, e-commerce, recommendation systems, knowledge graphs, and many others. Moreover, many of these involve graphs with textual attributes (i.e., lextual graphs), making them well-suited for LLM-centric methods. This has spurred interest combining graph-based technologies, particularly graph neural networks (GNNs), with LLMss enhance their reasoning graphs 44 15 24
The Present Work: Enabling 'Chat With Your Graph'. While existing works integrate LLMs and GNNs in various ways, they mostly focus on conventional graph tasks such as node, edge and graph
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Figure 1: We develop flexible annseiin- framework targeting real-world textual graph ap- plications via a unified conversational interface. Presented here are examples showcasing the model's adeptness in handling generative and creative queries in practical graph-related tasks: common sense reasoning, scene understanding, and knowledge graph reasoning, respectively.
classification (8) or answering simple questions on small or synthetic graphs 44
31 In contrast,
we develop flexible question-answeriig framework targeting complex and real-world graphs. This framework enables users to 'chat with their graph' via a unified conversational interface, representing a leap towards intuitive interaction with graph data, as demonstrated in Figure [
The Need for a Comprehensive Graph Benchmark. Question answering (QA) is fundamen- tally important task in natural language processing. serving as a key benchmark for assessing LLMs and providing a unified interface for various capabilities. Despite extensive research in QA, com- prehensive benchmark specifically tailored for the graph modality is lacking. In contrast to existing benchmarks that focus on basic graph-based reasoning tasks such as node degree, edge existence, and shortest path 66 44,, our benchmark addresses complex and real-world graph applications including common sense reasoning, scene understanding, and knowledge graph reasoning (refer to Figure 2). This is vital for measuring progress toward a model capable of answering a wide range of questions about graphs from diverse applications.
To enable effective and efficient graph QA, even on large graphs, we propose G-Retriever, a new framework combining the strengths of GNNs, LLMs, and RAG (Figure B). Next, we will discuss the motivation, strengths, and details of our model.
New Architecture for GraphQA.
Tackling Hallucination in Graph LLMs. LLMs are prone to hallucination, a phenomenon where the generated content is factually inaccurate or nonsensical [12 We validate the presence of this issue in graph settings. In particular, where a frozen LLM interacts with a trainable GNN that encodes graph data as a soft prompt, as in GraphToken (3I. Our findings, shown in Table I indicate that hallucination, an important problem in text-based LLMs, is also prevalent in Graph L.MMs. This may be attributed to the baseline's inability to recall the entire graph structure from single graph embedding, leading to the generation of incorrect nodes or edges during the QA task. In contrast, by employing RAG for direct information retrieval from the actual graph, our G-Retriever mitigates this issue, as substantiated by Table
employ baseline method that adapts MiniGPT-4 [57] to graphs,
Enhancing Scalability and Efficiency in Graph LLMs. Recent research endeavors have explored translating graphs into natural language, such as by flattening nodes and edges into a text sequence, enabling their processing by LLMs for graph- based tasks 6). However, this method faces critical scalability issues. Converting graph with thousands of nodes and edges into text sequence results in an excessive number of tokens, surpassing the input capacity of many LLMs. An alternative of truncating the graph text sequence to fit the LLM's input token limit leads to loss of information and response quality. G- G-Retriever overcomes these issues with its RAG component, which allows for effective scaling to larger graphs by selectively retrieving only relevant parts of the graph.
Tailoring the RAG Approach to Graphs. Existing RAG methodologies are primarily designed for simpler data types or knowledge graphs, where information is retrieved in manner isolated from the graph structure 7 36 16]. Hence, we introduce new retrieval approach for general textual graphs. Notably, we formulate subgraph retrieval as Prize-Collecting Steiner Tree (PCST)

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Table 1: Observation and mitigation of hallucination in graph LLMs.
slepharts
Happram
User
rating
Jushes
standing
on ol
What kind animal the bushes?
Answer the question and list the nodes or edges from the graph that support your answer.
Innerreet response with hallucinated nodes and edges)
The animal in the hushes is a deer.
Nodes:
Deer (node 1)
LLM Graph Prompt Tuning
Bushes
Edges:
Deer Rushes (edge 1)
Deer Grass (edgge 2)
Bushes - Grass (edge 3)
[Correct response with accurate node and edge references]
Based information provided the graph,
the bushes elephant.
The modes that support this answer
G- R-Retriever (Ours)
Node I: The elephant is mentioned in the first line of the graph.
The edges that support this
The edgo counects the clephant to the bushes, indicating that the clephant is the bushes. Therefore, answer the question The animal in the bushes is elephant.
optimization problem, which takes neighborhood information into account during retrieval. This also allows the return of a subgraph most relevant to query, thereby improving explainability.
The contributions of this paper are outlined as follows:
Pioneering the integration of Graph RAG. We present the first retrieval approach for general textual graph tasks, which greatly enhances scalability and efficiency.
Enabling *Chat with Your Graph', We develop a flexible question answering framework to handle complex and real-world textual graphs through unified conversational interface.
Introducing A Novel GraphQA Benchmark. We introduce a diverse benchmark targeted at real-world graph question answering. filling crucial research gap.
Empirical Findings. We demonstrate the efficiency and effectiveness of G-Retriever in multiple domains and present the significant finding of hallucination in graph LLMSs.
2
Related Work
Graphs and Large Language Models. significant body research has emerged at the intersection of graph-based techniques and LLMs [30
24
[5]
44
[54] This exploration spans diverse aspects,
ranging from the design of general graph models [47
25
51
19
40 31, and multi-modal architec-
tures 23
49 to practical applications. Noteworthy applications include fundamental graph reason- 33 graph classiiication/regrsssoo (2.55] (5),
ing 522 3 [6]. node classification [8
11 39
[5
and leveraging LLMs for knowledge graph-related tasks
29
Retrieval-Augmenteed Generation (RAG). The concept Retrieval- Aeermeeal-uugmenteed Generation, initially proposed by Lewis et al. [21]. has gained increased attention for its ability to mitigate the issue of hallucination within LLMs and enhance trustworthiness and explainability [7]. Despite its success in related tasks, the application of retrieval based approaches general graph tasks remains largely unexplored. Most existing work focuses primarily on the knowledge graph [3]] 36 [6].
Our research is the first to apply retrieval-basee approach to general graph tasks, marking novel advancement in the field and demonstrating the versatility of RAG beyond language processing.
Parameter-Efficient Fine-Tuning (PEFT). The field of LLMs has witnessed significant advance- ments through various parameter-efficient fine-tuning techniques. These methodologies have played a crucial role in refining L.MMs, boosting their performance while minimizing the need for extensive parameter training. Notable among these techniques are prompt tuning, as introduced by Lester al. (20), and prefix tuning, proposed by Li and Liang (22). Furthermore, methods like LoRA [10),
3

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and the LLaMA-adaples [53, have been influential. These advancements in PEFT have laid the foundation for the development of sophisticated multimodal models. Prominent examples in this domain include MiniGPT-4 [57]. LL 266.. and NExT-Chat [46] There are also emerging efforts in applying PEFT to graph LLMs, such as GraphLLLM K and GraphToken
31 for basic graph
reasoing tasks and GNP 41 for multi-option QA on knowledge graphs.
3
Formalization
This section establishes the notation and formalizes key concepts related textual graphs, language models for text encoding, and large language models and prompt tuning.
Textual Graphs. A textual graph is graph where nodes and edges possess textual attributes. Formally, it can be defined as = (V, E, (nv, fie JeEE), where and E represent the sets of nodes and edges, respectively. Additionally. In D and ze D denote sequential text associate with a node n E V or an edge E E, where represents the vocabulary, and Ln and Le signify the length of the text associated with the respective node or edge.
Language Models for Text Encoding. In the context of textual graphs, language models (LMs) are essential for encoding the text attributes associated with nodes and edges, thereby learning representations that capture their semantic meaning. For a node with text attributes In DLu, an LM encodes these attributes as:
Zn LM(In) R
(I)
where Zn is the output of the LM, and d is the dimension of the output vector.
Large Language Models and Prompt Tuning. LLMs have introduced a new paradigm for task- adaptation known "pre-train, prompt, and predict", replacing the traditional "pre-train, fine-tur paradigm. this paradigm, the LLM first pre-trained on a large corpus of text data to learn general language representations. Then, rather than fine-tuning the model task-specific labeled data, the model is prompted with a textual prompt that specifies the task and context. Subsequently, the model generates the output directly based the prompt and the input.
The LLM, parameterized by weights 0, takes a sequence of tokens X, and a prompt P as input, and generates sequence of tokens Y {y1, 3/...... : 3r as output. Formally, the probability distribution of the output sequence given the concatenated input sequence and prompt, i.e., [P; X]. defined as
po (P(
II (o (P;
(2)
Here, Yoi represents the prefix of sequence y up to position i - 1, and p(u XI) represents the probability generating token yi given yei and [P:
Soft prompt tuning eliminates the need for manual prompt design. Given series of p tokens X = (21, .2, Tpp. after being processed by the text embedder, it forms a matrix X, E Rox4, where di is the dimension of the embedding space. Soft prompts can be represented as parameters Pa E R2 where ş is the length the prompt. The prompt then concatenated with the embedded input, forming single matrix XP] g(++p) xd: This combined matrix is processed by the self-attention layers in LLM as usual. Training involves maximizing the likelihood of Y through backpropagation, with gradient updates applied solely to Pe, while 0 remains fixed.
Proposed GraphQA Benchmark
Our GraphQA represents a comprehensive and diverse benchmark for graph guestion-answering. is tailored to assess the capabilities of models in answering wide range of questions about graphs across diverse domains.
4.1 Data Format
Fach entry the GraphQA benchmark consists a textual graph, a question related the graph, and one more corresponding answers, illustrated in Figure 2

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Table Summary of datasets used in GraphQA benchmark.
Dataset
ExplaGrapha
SceneGraphe
WebqSP
#Giraphs
2.766
100,000
4,737
Avg. #Nodes
5.17
19.13
1370,89
Avg. #Edges
68.44
4252.37
Node Attribute
Commonsense concepts Commmnnsense relations Common reasoning Accuracy
Object attribures (e.g. color, shape) Relations (e.g., actions, spatial relations) Scene graph question answering Accuracy
Entities in Freebase
Edge Attrbute
Relations Frocbase
Task
Knowledge based question answering Hit@
Evaluaiio Matrix
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Figure 2: Illustrative examples from the GraphQA benchmark datasets.
Textual Graphs. The textual graph is converted into natural language format, resulting in list of nodes and edges, akin to CSV file format. It is important to note that while multiple methods exist for textualizing a graph, our focus is not on identifying the optimal solution. Instead, we prioritize a straightforward yet empirically effective approach for representing graphs in natural language, facilitating the benchmark's use in diverse GraphQA scenarios.
Questions and Answers. Questions are designed to explore specific elements or relationships within the graph. Answers, residing within the attributes of nodes edges, often require multi-hop reasoning for accurate identification.
4.2 Description of Datasets
The GraphQA benchmark integrates three existing datasets: ExplaGraphs, SceneGraphs, and WebQSP. presents the summary statisties of these datasets. It is important note that these datasets were not originally developed for this work. However, a significant contribution of our research is the standardization and processing of these diverse datasets into uniform data format suitable for the GraphQA benchmark. These datasets, previously utilized in different contexts, are reintroduced with new focus tailored for GraphQA. For detailed comparison with the original datasets, see the Appendix C
ExplaGraphs is dataset for generative commonsense reasoning, focusing on creating explanation graphs for stance prediction in debates. It offers detailed, unambiguous ceammonnsnne-uuggentte graphs to evaluate arguments supporting or refuting belief. The primary task is to assess whether arguments are supportive or contradictory, using accuracy as the metric. We have converted the triplet-form provided in Saha et al. [35] into a standard graph format.
SceneGraphs, a visual question answering dataset, includes 100,000 scene graphs. Each graph details objects, attributes, and relations within image. This dataset challenges users with tasks requiring spatial understanding and multi-step inference. The task is to answer open-ended questions based textual description of scene graph, evaluated on accuracy. We have sampled from the GQA dataset [3] and constructed standard graphs from the provided JSON files.
WebQSP is a large-scale multi-hop knowledge graph QA dataset consisting of 4,737 questions. It was proposed by Yih et al. [48] and, following Luo et al. (28), utilizes a subset of Freebase, encompassing facts within 2-hops of entities mentioned in the questions. The task involves answering questions that
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Figure 3: Overview of the proposed G-Retriever: 1) Indexing: Graphs are indexed for efficient query processing: 2) Retrieval: The most semantically relevant nodes and edges are retrieved, conditioned on the query; 3) Subgraph Construction: A connected subgraph is extracted, covering as many relevant nodes and edges as possible while maintaining manageable graph size; 4) Generation: An answer is generated using a 'graph prompt', textualized graph, and the query.
require multi-hop reasoning, Given the possibility of multiple answers for the same question, the hit@1 metric is used to assess the precision of the top returned answer.
5 G-Retriever
In this section, we introduce G-Retriever, a new architecture tailored for GraphQA, which integrates the strengths of GNNs, L.MMs, and RAG. To allow efficient fine-tuning while preserving the L.MM's pretrained language capabilities, we freeze the L.M and use soft prompting approach on the output of the GNN, Our RAG-based design mitigates hallucinations through direct retrieval of the graph, while allowing our approach to scale to graphs exceeding the LLM's context window size. To adapt RAG to graphs, we formulate subgraph retrieval as a PCST optimization problem. This approach also allows us to enhance explainability by returning the retrieved subgraph.
G-Retriever comprises four main steps: indexing, retrieval, subgraph construction and generation, as depicted in Figure 3 The implementation details of each step are elaborated in the following sections.
5.1 Indexing
We initiate the RAG approach by generating node and graph embeddings using a pre-trained LM. These embeddings are then stored in a nearest neighbor data structure.
To elaborate, consider Vn E D4n as the text attributes of node n. Utilizing pre-trained LM, such as SentenceBerr (34). we apply the LM to x. yielding the representation z,j
LM( RRd,
where d denotes the dimension of the output vector. Similar preprocessing steps are applied to edges. Refer to Figure 3 Step for an illustrative representation.
5.2 Retrieval
For retrieval, employ the same encoding strategy to the query zig. ensure consistent treatment of textual information:
2n = LM(,.) E Rdd
Next, to identify the most relevant nodes and edges for the current query, we use a k-nearest neighbors retrieval approach. This method yields of "relevant nodes/edges' based on the similarity between the query and each node or edge. The retrieval operation is defined as:
V argtopknev cos(2q. 2n)
(5)
E = argtopk cos Zg: ze);

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where and ze are the embeddings of node n and edge e, respectively. We use the cosine similarity function, cos(..)), to measure the similarity between the query representation and the node/edge embeddings. The argtopk operation retrieves the top-k elements based on this similarity, providing set of nodes V2 and edges Fgg. considered most relevant to the query. See Step 2 of Figure
5.3 Subgraph Construction
This step aims to construct a subgraph that encompasses as many relevant nodes and edges as possible, while keeping the graph size manageable. This approach offers two key benefits: Firstly, it helps to filter out nodes and edges that are not pertinent to the query. This is crucial because irrelevant information can overshadow the useful data, potentially diverting the focus of the subsequent LLM from the information of interest. Secondly, it enhances efficiency; by keeping the graph size manageable, it becomes feasible translate the graph into natural language and then input it into the LLM for processing. The Prize- Collectcl Steiner Tree algorithm [2] serves our primary method for identifying such optimally sized and relevant subgraphs. See Step in Figure 3
Prize-Collecting Steiner Tree (PCST). The PCST problem aims to find connected subgraph that maximizes the total prize values of its nodes while minimizing the total costs of its edges. Our approach assigns higher prize values to nodes and edges more relevant to the query, as measured by cosine similarity. Specifically, the top k nodes/edges are assigned descending prize values from down to I, with the rest assigned zero. The node prize assignment is as follows:
k i, if E Vy and is the top node,
prize(n) 20,
(6)
otherwise.
Edge prizes are assigned similarly. The objective is to identify subgraph, S (V*, E*), that optimizes the total prize of nodes and edges, minus the costs associated with the size of the subgraph:
S
argmax SCG. connected nEVS
Σ
prize(n) + Σ prize(e) cost(S),
eEFS
where
cost(S) = |Es x Ce.
(8)
and C2 denotes a predefined cost per edge, which is adjustable to control the subgraph size.
The original PCST algorithm designed for node prizes only. However, given the significance of edge semantics in certain scenarios, we adapt the algorithm to accommodate edge prizes as follows: Consider an edge e with a cost C. and a prize Pe. Cf > Pe. can be treated as a reduced edge cost of C. P.. However, if Pe > Ce. negative edge costs not allowed in the original algorithm. Our solution involves replacing edge e with a 'virtual node' ve. connected to both endpoints of e. This virtual node is assigned prize of P. Cc, and the cost of the two new edges leading to the virtual node is set to zero. This modification effectively mirrors the original problem, as including edge e in the original graph is analogous including the virtual node in the modified graph. Finally, optimize the PCST problem using a near-linear time approach
5.4 Answer Generation
Graph Encoder. Let S* = (V*, E²)) represent the retrieved subgraph. We use graph encoder to model the structure of this graph, specifically using a standard Graph Attention Network (GAT) [43 Our approach for encoding the retrieved subgraph is defined as follows:
hg PONL, (S*)) R
Here, POOL denotes the mean pooling operation, and d, is the dimension of the graph encoder.
Projection Layer. We incorporate multilayer perceptron (MLP) to align the graph token with the vector space of the LLM:
Kg MLP dz (h2) E Rd4,
(10)
where di is the dimension of the LLM's hidden embedding.
Text Embedder. To leverage the text-reasoning capabilities of L.MMs, we transform the retrieved subgraph S* into textual format. This transformation involves flattening the textual attributes of the
7

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nodes and edges, as illustrated in the green box in Figure 2 Subsequently, we combine the textualized graph with the query to generate response. Let xy denote the query; we concatenate it with the textualized graph textualize embedding h4 using a text embedder, which is the first layer of a pretrained and frozen L.M::
We refer to this operation as textualize( (-).
We then map the result to an
h
xg))) IR fxx,
(11)
where [:
represents the concatenation operation, and L is the number of tokens.
LLM Generation with Graph Prompt Tuning. The final stage involves generating the answer Y given the graph token hg. acting as a soft prompt, and the text embedder output hę- These inputs are fed through the self-attention layers of pretrained frozen LLM, with parameter 0. The generation process is represented as follows:
P0 (YIS", = II pz (Vilves, (hy; ha]): i=1
(12)
where 1 ha] concatenates the graph token hy and the text embedder output hę. While 0 is frozen, the graph token hg receives gradients, enabling the optimization of the parameters of the graph encoder Φ and the projection layer Φ2 through standard backpropagation.
Experiments
6.1 Experiment Setup
In the indexing step, SentenceBert 34
the LM encode all node and edge attributes. In
the generation step, we use the open-source Llama2-7b 42 as the LLM and Graph Transformer [37] the graph encoder. Additional details are provided in Appendix B.
6.2 Main Results
In our experiments, we consider three model configurations: 1) Inference-only: Using frozen LLM for direct question answering: 2) Frozen LLM w/ prompt tuning (PT): Keeping the parameters of the LLM frozen and adapting only the prompt; 3) Tuned LLM: Fine-tuning the LLM with LoRA [TO]. We provide more details in Appendix B.2
Table 3: Performance comparison
ExplaGraphs, SceneGraphs, and WebQSP datasets for different configurations, including Inference-only, Frozen LLM with prompt tuning (PT), and Tuned LLM settings. Mean scores and standard deviations (mean std) are presented. The first best result for each task is highlighted in bold and the second best result is highlighted with an underline.
Sctting
Method
ExplaGraphs
SceneGraphs
WebQSP
Zero-shot
0.5650
0.3974
41.06
Zero-CoT
18
0.5704
0.5260
51.30
Inference-only
CoT-BAG
0.5794
0.5680
39.60
KAPING
0.6227
0.4375
52.64
Prompt tuning
0.5763 0.0243
0.6341
0.0024
48.34 + 0.64
GraphToken 31
0.8508 0.0551
0.4903 0.0105
57.05 0.74
Frozen LLM w/ PT
G-Retriever
0.8516 0.0092
0.0162 0.8131
70.49 + 1.21
APrompt mning
1 47.77%%
28.23%
1 45.81%
LoRA
0.8538 0.0353
0.7862 + 0.0031
66.03 4 0.47
Tuned LLM
G-Retriever w/ LoRA
0.8705 0.0329
0,8683 0.0072
73.79 + 0.70
A LoRA
1.95%
11.74%
1 10.44%
Table 3 demonstrates the effectiveness of our method across three datasets in various configurations. In the inference-only setting, G-Retriever surpasses all baselines. Notably, LLM can perform even better when no graph knowledge is provided (i.e., question only), which might be attributed to the complexity and potential noise in the knowledge. For frozen LLM with prompt tuning, G-Retriever outperforms traditional prompt tuning and GraphToken (31. graph prompt tuning-based method, with average performance increases of 40.6% and 30.8% respectively. Furthermore, when tuned with LoRA. G-Retriever achieves the best performance.

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Table Retrieval on graphs significantly improves efficiency.
Before Retrieval (Avg.)
After Retrieval (Avg.)
Dataset
# Tokens
# Nodes
Min/Epoch
# Tokens
# Nodes
Min/Epoch
SceneGraphs WebQSP
1,396 100,627
19 1,371
123.1
235 (183%)
5 (174%%
86.8 (1.29%)
18.7
610 (199%)
18 (199%)
6.2(167%)
6.3 Efficiency Evaluation
The efficiency of our approach is highlighted by the data in Table 4 Implementing our graph-based retrieval significantly decreases the number of tokens required to describe the graphs in text, reduces the number of nodes in graphs, and speeds up the training process. Specifically, for the SceneGraphs dataset, tokens decreased by 83%, nodes by 74%, and training time by 29%. For the WebQSP dataset, tokens decreased by 99%, nodes by 99%, and training time by 67%. These substantial reductions demonstrate the method's efficiency and potential in managing large-scale graph data.
6.4 Mitigation of Hallucination
To evaluate hallucination, we instructed the models to answer graph-related questions, specifically by identifying supporting nodes edges from the graph. We assessed the model's faithfulness using three metrics: the fraction of valid nodes (denoted as Valid Nodes), the fraction of valid edges (denoted as Valid Edges), and the fraction of times the entire set of cited nodes and edges was valid (denoted as Fully Valid Graphs). We manually reviewed 100 responses from both our method and the baseline (i.e., L.M with graph prompt tuning). Table Jhoows that G-Retriever significantly reduces hallucinations by 54% compared to the baseline, our graph retrieval ensures that the data sourced directly from the actual graph, leading to fewer hallucinations. See Appendix G for details.
6.5 Ablation Study
In this ablation study, we assess the individual impact of key components within our pipeline. As shown in Table [ there are performance drops when any of these components are removed, with the graph encoder and textualized graph showing declines of 22.51 % and 19.1 19.19%, respectively. This demonstrates their complementary effects in representing the graph in both textual and embedded formats. Additionally, the retrieval on graphs is also important to the overall performance. Further details are available in Appendix B.3 We also present additional studies on our framework: it is robust to the choice of graph encoders (see Appendix B.4] and benefits from the increased scale of LLMs (see Appendix B.5)
Table 5: Hallucination reduction on the SceneGraphs dataset, measured by fractions of valid nodes, valid edges, and fully valid graphs (where all nodes and edges are correct).
Table 6: Ablation study on the WebQSP dataset showing performance drops (Hit@) 1) when each component is removed.
Method
Hit@1
Baseline
G-Retriever
G-Retriever
70.49
Valid Nodes
31%
77%
w/o Graph Encoder
$4.62 (122.51%)
Valid Edges
12%
76%
w/o Projection Layer
69.70 (11.1 %)
Fully Valid Graphs
8%
62%
w/o Textualized Graph
56.96 (L19. 19%)
w/o Retrieval
63.84 (19.4335)
Additionally, we include detailed comparison with existing retrieval methods (see Appendix Dj. discussion on the complexity (see Appendix F and demonstrations on how to use G-Retriever to 'chat with your graph' (see Appendix H)

---

7 Conclusion
In this work, introduce new GraphQA benchmark for real-world graph question answering and present G-Retriever, an architecture adept at complex and creative queries. Experimental results show that G-Retriever surpasses baselines in textual graph tasks across multiple domains, scales effectively with larger graph sizes, and demonstrates resistance to hallucination.
Limitations and Future Work: Currently, G-Retriever employs a static retrieval component. Future developments could investigate more sophisticated RAG where the retrieval is trainable.
Acknowledgment
BH is supported by the Ministry of Education, Singapore, under the Academic Research Fund Tier (FY2023) (Grant A-8001996-00--000 authors would like to express their gratitude the reviewers for their feedback, which has improved the clarity and contribution of the paper.
XB is supported by NUS Grant ID 1--252-000--97-133- The
10

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A Impact Statements
As LLMs are applied to increasingly diverse tasks, their ability to process complex structured data will be increasingly vital. Our work aims to enhance LLMs' ability interact with graph-structured data, while resisting hallucination, thus improving model reliability. We also enhance explainability, both by returning the retrieved subgraph, and through the use of conversational interfaces for 'chatting with graph', which allows for better human-Al interaction and for models to behave in way that is more allgnal with human expectations.
B Experiment
B.1 Implementation Settings
Experiments are conducted using 2 NVIDIA A100-80G GPUs. Each experiment is replicated four times, utilizing different seeds for each ensure robustness and reproducibility.
Graph Encoder. We use Graph Transformer [37 as the GNN backbone, Our configuration employs 4 layers, each with 4 attention heads, and hidden dimension size of 1024.
LLM. We use the open-sourced Llama2-7b 42 as the L.M backbone. fine-tuning the LLM with LoRA [10]. the lora_r r parameter (dimension for LoRA A update matrices) is set to 8, and lora_ alpha (scaling factor) set to 16. The dropout rate set to 0.05. prompt tuning, the LLM is configured with 10 virtual tokens. The number of max text length is 5 512, the number of max new tokens, i.e. the maximum numbers tokens to generate, is 32.
For retrieval over graphs via PCST, for the SceneGraphs dataset, we select the top k nodes and edges, setting
PCST,
Here, the cost of edges, denoted Ce, is set 1. Regarding the WebQSP dataset, we set k = 3 for nodes and & 5 for edges, with the edge cost, Ce. adjusted to 0.5. For the ExplaGraphs dataset, which is characterized by small graph size averaging 5.17 nodes and 4.25 edges (as detailed in Table 2). the entire graph can fit in the LLM's context window size. Consequently, we aim to retrieve the whole graph by setting i O, effectively returning the original graph unaltered.
optimizer. We set the initial learning rate le-5, with weight decay of 0.05. The learning rate decays with a half-cycle cosine decay after the warm-up period. The batch size is 4, and the number of epochs 10. To prevent overfitting and ensure training efficiency, an early stopping mechanism is implemented with patience setting of 2 epochs.
Optimization. We the AdamW 27
B.2 Details of Model Configurations
In our experiments, consider three model configurations:
!) Inference-only: Using a frozen LLM for direct question answering with textual graph and question, see Figure
LLM
&
Quorse: Wha nonon Anever
Sextradleee (S)
quary 4
Figure 4: Model configuration 1) Inference-only.
Zero-shot. In this approach, the model is given textual graph description and task deseription, and is immediately asked to produce the desired output. No additional examples demonstrations are provided.
15

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ZoTo-CoT. Zero-shot Chain-of of thought (Zero-CoT) prompting [8] is a follow-up up to CoT prompting (45,, which introduces an incredibly simple zero shot prompt by appending the words "Let's think step by step." to the end of question.
CoT-BAG, Build-a-Graph Prompting (BAG) is prompting technique that adds "Let's construct graph with the nodes and edges first." after the textual description of the graph is explicitly given.
KAPING. KAPING [ is zero-shot knowledge -ageeddge--gmeeeedd prompting method for knowl- edge graph question answering. It first retrieves triples related to the question from the graph, then prepends them to the input question in the form of prompt, which then forwarded to LLMs to generate the answer.
Frozen LLM w/ prompt tuning (PT): Keeping the parameters of the LLM frozen and adapting only the prompt. This includes soft prompt tuning (see Figure 5a GraphToken 31 which is graph prompt wning method, and our G-Retriever method (see 5bg
3 tinak
LLM
LLM
1S6 IScEnnsiio Lucco
551
queyg
(P",at)
lestaalee(5
(a) Prompt Tuning
(b) G-Retriever
Figure Model configuration 2) Frozen LLM w/ prompt tuning.
3) Tuned LLM: Fine-tuning the LLM with LoRA. This includes standard fine-tuning of an LLM for downstream tasks using LoRA (see Figure ba) and G-Retriever with LoRA (see Figure 6b).
mile sikee
LIM
JIA Hale dhe
All Alldikea-2
spary
Acrest
(a) LoRA
(b) G-Retriever w/ LoRA
Figure 6: Model configuration 3) Tuned LLM.
B.3 Details of Ablation Study
This section illustrates the modifications made to the original architecture in the ablation study, as presented in Figure
Without Graph Encoder (w/o GraphEncoder): In this setting, we replaced the graph encoder with trainable soft tokens, setting the number of these virtual tokens to 10.
Without Projection Layer (w/o Projection Layer): Here, we removed the projection layer following the graph encoder. We configured the output dimension of the graph encoder to be 4,096, matching the hidden dimension of Llama2-7b. This allows the output graph token (the yellow token in Figure 7b) to be concatenated directly with the LLM tokens (blue tokens).
Without Textualized Graph (w/o Textualized Graph): In this configuration, we modified the textual input the LLM.. Rather than using a combination the question and the textualized graph, solcly used the question.
16

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(a) w/o GraphEncoder
(b) w/o Projection Layer
(c) w/ Textualized Graph
Figure 7: Ablation study configurations.
B.4 The Choice of Graph Encoder
In addition to the Graph Transformer 137 we explore other GNNs as the graph encoder, such as GCN [17] and the GAT [4]. The comparative results of these models on the WebQSP and ExplaGraphs datasets are presented in Table
Table Performance different graph encoders on the WebQSP and ExplaGraphs datasets.
Graph Encoder
WebQSP
ExplaGraphs
GCN 17
70.70
0.8394
GAT
43
70.27
0.8430
Graph Transformer
70.49
0.8516
The results demonstrate that our proposed method exhibits consistent robustness across different graph encoders. Notably, all three encoders GCN, GAT, and GraphTransformer demonstrate competitive and closely aligned performance on the WebQSP dataset, with Hit@) scores of 70.70, 70.27, and 70.49, respectively. However, the performance differentiation becomes more pronounced on the ExplaGraphs dataset, where by GAT and GCN with scores of 0.8430 and 0.8394, respectively. This variation performance across the datasets highlights the importance of encoder selection based on the specific characteristies and requirements of the dataset.
exhibits superior Hit@) score of 0.8516, followed
B.5 The Choice of LLM
As for the choice of LLM, we considered both Llama2-7b and Llama2-13b. Our experiments demonstrate that stronger LLMs enhance the effectiveness of our method, as shown Table S indicating that it benefits from the increased scale of the LLMs.
Table Performance of different LLMs on the WebQSP dataset.
LLM
Llama2-7b
Llama2- 13b
Hit@1
70.49
75.58
C GraphQA Benchmark
In this section, we detail how our GraphQA benchmark differs from the original datasets, including the specific processing steps we employed. For concrete examples that illustrate the differences between the raw text in the original dataset and in our GraphQA benchmark, please refer Table 9
ExplaGrapha The original dataser T ized this format by converting the triplets into a graph representation. Specifically, each head and tail in triplct is transformed into node, and the relation is transformed into an edge. Since the test dataset labels are not available, we have utilized only the training and validation (val) datasets from the original collection. We further divided these into training, val, and test subsets, using 6:2:2 ratio.
335 represents relationships using triplets. We have standard-
"nttps//eepplggraphss. itt.hh/pppgggaapsssiuubbibbboooo
17

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Table Comparison of text formats in original datasets and our GraphQA benchmark.
Datase
Original dataset
GrapoQA Benmark
mode ilaode strinn Deninppeentón 1 l.being sbusedin 2 2polioein Sharmin 4.pooplein S.eitizennin
centrapment; csgable of, being ahosod) (twing abused: created by: pollice) (police; capable of: harm) (ham; used for. people) (people: part citizens)
ExplaGrapha
src.edge, altzdsin of Loreased by.2 2.cupable of,3 3,ased for,Fin 4gunt of.5in
"width" "objeess"; 16812677; "moms"" "bantea", 54, "relations" ("objeer":
"681262", "kame"; "to the left of"). 'w": 64, "anihutes": ["small" "yellow"] " "y': 55, "x"t 248, "6812655; "name": "sputs", "W's 16, "relations": []. "w": 26.
"attebutts" 1y:
245, "6812647; "name"; "Daranas", "h": 50, "velation" "objecr": "681259". "same"; "to the left of"). "w": "amiibttes": ["small".
"yellow"] "y's 32, "s" 268, 168 1815255; "same": "pienie", "W:: 374, "relations" D]. "w": 499, "utributes: ['delicioos") J. yn n. "x"t O. "681362" "nam"": "staw"" "h": 95, "relations:: ('objerr": 7681268", "name"; "to right of, "object": "681267"
modd_jd_moxk__at
"strihatest: ('Whiie", "plastic"] yn, 55, 'x": 402, 1681261" "name" "mear", "Mh": 27. "robsions": ("object'; "681255", "name": "on". "ubject": "681255", "nume": "inside"], 24, "smiibones ['small" "boow". "dalicsons]] "y": 123, 68, "681260" "ham"" "riee" "I" 57, "selations": ['objeet" "681255", "mnaee" "on". "objeat; "681258". "nmmet "otteleff of]. "w": 93, "atributes l'siled". "white"]. "]. "y': 162, "x": 57, "6813997 "name": "emices"" "i' 16, "rclition" [] "w": 24.
"name": "to the right of, "ebject"; "681253", "name",
right of]. "w": 15,
I. T'aam:: sposs: (x.y.whi: (245, 92, 26, 16)"
mamee: bananz; attribute: small, yellow; (s.).w.h): (248, 55, 64,
2. "namme bananas; uttribute: small. yellow; Duyooal: (268, 32, 59,5""
"namm: pienic: altribune: deliciioss (sy.o.h::
Thame: straw; atribute: whise, plaase: (x.g..b): (402, 55, 95y"
5. "nume: meat, ultribune: small, brown, delicious: (5.y.dd): (68. 123, 24)
6. "name: rice; attrbuue: piled, white: (s.youht:: (57, 162, 93,57)"
"atributes": ["greas"], 'y: 147, "xt 90, "681268" "name": "tableckohh" "h"
"'name: onions; ammiute: green: (x.y,chl::
147, 16)"
"rolations;; : ["objeat": "68 181222", "nome"; "to the of], "w" 396, "attributes!"
["uhie") J. "y" "y"t o, "681258" "same" "houl", "I" 99, "malations" [ ["obgeer":
"namm: bow); alimibute full: (s.yowh): (178, 184, 115, 99y"
"aame: tablocloth; atribute: White: 00.0,0h1: 0, 0.,39,2277)
"681255". 'mmme: "nex to".. "object': 588257", hamee: : "uf". "object": '681255".
10," name: plantains; atribune: red: (R.y.uub:: (346, 0, 45, 70y"
SccueGraphs
"name"s "near", "ubject": "681256", "name": "to right of"" "object" 1681 "681260".
11.
speen: uttribute: large,
salver, (x.y,uh): 196, 140.65y
"same"s "to the right off, "ohjoct": "681255", "same": "to right of") 115,
12' name: dish, attiluue:: cream colored: (R.y.wd)): (187, 199, 108, 81)"
"atributes": :: ["fil"", 'y: 184, 'xt 178, 1681259;; "nume": "planuiins". "h": 70.
13, name: meal; (x.yowdr:: (58. 121, 150, 111y"
"rolations:: ('oggea": "681264", "nome'; "to the right of "w": 45, "attributes";
14, name: plate; auriute: white. full; (s.yo.hh: (30. 111, 176, 138)' yellow, (x.g,ub):
["red"] "y": "x: 346, "681256": "nome": "spoon" "h": 65, "melaionn" ("abject":
banana, amiihute see, stc.edge_.u attr.dat
15,
87, 73,
"681255". "mme": "on". "object": '681257" "name": : "Iu the lefl of. "object": "68l 255", "nume": "in". "objer"; "681258", "same": "to the left of"). "w": 140.
0,u the left of,4 2,10 the left 10 4,10 the right of.8 4.to the right of 4,to the right of S,on, S.iniide. 14th c,oa, 14th 6.50 the left of 8,to the left ot 9.nest 10.14 9,0.122 14 9.00 the right of, I'n steo rightt of
"attrilutes": ("luge" "mera"" " "slver)) J. 'y: 196, "s's n. "681257"; "name"" "diah". 81. 'relations': [Tobject': "681258" "mame": "inside", "object": "681256".
"name": "to the right of: "ohjoct": "681258", "mome" "in", "ohjoct" 1681 2255"
9ut the right of 10ss the right of jon
1,3n the of, 12 lin. 14in Lno the left ot,9tn 12.isadee 9in 12.00 the right of. 12,in,5in 12.to the right
"nume": "to the rightef)) "w": 108, "auributes:: ( ('treum colored 1. "y: 199, "x": 187, "681254"; "nume"; "meal". 'b': III, "relations": [l. "w": 130, "itributees"
of14 14,00 the left o2, 14, 14,wthh 14.near 14,00 the left of,96a
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SceneGraphs. The original GQA dataset is designed for tional question answering, aiming to address key shortcomings of previous VQA datasets [[3]. It comprises 108k images, each associated with a Scene Graph. In our study, we focus differently on graph question answering; hence, we did not utilize the image counterparts, leveraging only the scene graphs from the original dataset. Additionally, the original dataset describes images using JSON files. We simplified the object IDs to suit our research needs. We randomly sampled 100k samples from the original dataset and divided them into training, validation, and test subsets, following 6:2:2 ratio.
visual reasoning and composi-
WebQSP. We follow the preprocessing steps from Roc 28 The original dataset uses a list of triplets format, which we have transformed into our unified graph format. Furthermore, to avoid
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discrimination between capital and lowercase words, we have converted all words to lowercase. We used the same dataset split as in the original dataset.
Contribution of the GraphQA Benchmark. We acknowledge that the GraphQA benchmark involves converting three existing graph datasets into uniform format. However, believe this standardization provides significant value to the research community in several ways:
Task Introduction:
Unlike existing graph question-answering benchmarks that focus on small or synthetic graphs, our benchmark includes real-world applications and frames them as graph question-answering tasks.
Standardization: A key and significant effort of this benchmark is the standardization and processing of diverse datasets into uniform format suitable for GraphQA tasks. These datasets, previously used in different contexts, are redesigned to focus specifically on GraphQA, ensuring consistent and comparable evaluations across models.
Accessibility: We have open-sourced the GraphQA benchmark, providing unified format that simplifies model application across multiple datasets. This reduces the complexity of handling various data structures and preprocessing pipelines, lowering barriers for new researchers and encouraging broader participation. We have already seen several novel works using our GraphQA benchmark, and we expect rapid adoption within the LLM and GNN communities.
The benchmark offers baseline performance metrics, helping researchers identify the strengths and weaknesses of new approaches compared established baselines.
Baseline Comparisons:
D Graph Retrieval-Augmented Generation (GraphRAG)
D.1 Elaboration on PCST-Based Retrieval
Modeling motivation. We formulate subgraph retrieval Prize-Collecting Steiner Tree (PCST) optimization problem. This is motivated by the need to find connected subgraph containing most relevant nodes and edges, goal that aligns well with the objectives of PCST: maximizing node values while minimizing edge costs. Though not universally acknowledged as optimal, we have empirically validated its effectiveness.
Effectiveness compared other retrieval baselines. To validate the effectiveness of our PCST- based retrieval approach, we compared it against several baselines: (1) top-k triples retrieval, ie., KAPING D. which retrieves the top-k triples related to the query and incorporates them into the prompt for the LLM; (2) top-k nodes plus neighbors, which retrieves the top-k nodes and their one-hop neighbors, capturing local context; (3) shortest path retrieval, which retrieves the top-k nodes and computes the shortest paths between them.
For all methods. we & 5 and used Hamaa-7b-chie as the LLM. The results, presented in Table 10 show that our PCST-based retrieval method achieves the highest accuracy (Hit@) 1) of 67.17% on the WebQSP dataset, outperforming all baseline methods.
Table 10: Comparison of retrieval methods on the WebQSP dataset.
Method
@it@ 1
PCST retrieval
66.17
top-k triples retrieval (KAPING)
52.64
top-k nodes plus its neighbors
49.82
shortest path retrieval
55.20
D.2 Advantages of Subgraph-Ban Based Retrieval
Releext- Selecting nodes and edges in isolation may overlook neighborhood information. In contrast, PCST-based retrieval is guaranteed to return connected subgraph, capturing the graph
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context during the retrieval process. This approach retrieves not only high-relevance nodes or edges but also "bridge" elements that connect these with contextually sigmificant nodes or edges, which are crucial for generating comprehensive response.
Size Management. Compared the shortest path method, PCST retrieval provides greater control over the size of the retrieved subgraph. By adjusting the prizes and costs on nodes and edges, users can fine-tune the subgraph's extent. contrast, the shortest path approach lacks the ability control the distance between the top-k nodes, which can lead to disconnected subgraphs or the inclusion of unnecessarily long paths.
D.3 The Impact of K for Retrieval
We identify the most relevant nodes and edges and use a k-nearest neighbors retrieval approach (see Equation (). Small values may omit crucial knowledge information relevant to the query, while large k values could introduce excessive information, distracting the model from the essential details. To evaluate the impact of the number of k, we have conducted additional experiments by varying the choice of k to 3, 5, 10, and 20.
Table 11: The impact of k on the webqsp dataset.
k
3
5
10
20
Hit@1 0.6977
0.7063
0.7248
0.7039
As shown in Table the Hit@1 metric initially rises for small k values, peaks certain point, and then declines for large k values. Determining the optimal k value can be achieved through techniques like cross-validation using validation set.
D.4 The Choice of Similarity Function
The choice of similarity function is also important. In this work, we use cosine similarity, a widely adopted metric for measuring vector similarity in models that process vision and language. For instance, CLIP also employs cosine similarity to assess the similarity between text and image features. Although might not be the optimal choice, we believe that cosine similarity is general, representative, and valid choice for facilitating fast retrieval tasks.
E Discussion on the Complexity
F.1 The integration of GNNs, LLMs and GraphRAG
G-Retriever is framework integrate the strengths of GNNs, LLMS and GraphRAG The LLM++ framework, which involves enriching L.MSs with multi-modal capabilities by integrating an L.LM with encoder from another modality, is widely adopted approach. Notable examples include Llava, MiniGPT-4, and Flamingo, among others. They are not complex in terms understanding or implementation. Regarding the integration of GraphRAG, it does not require training and can be implemented during the preprocessing stage or on the fly. This approach does not significantly increase time complexity the size of the graph (e.g., eliminating 99% nodes in the WebQSP dataset), which in turn speeds up the overall running time (e.g., reducing it from 18.7 min/epoch to 6.2 min/epoch on the WebQSP dataset).
computational complexity. On the contrary, it can substantially reduce
E.2 Computational Resources
Utilizing two A100 GPUs, each with 80GB of memory, conducted tests Llama2-7b and WebQSP datasets. Our experiments had training batch size 16 and an evaluation batch size of 32, yielding the following results.
These results highlight efficiency improvements via graph RAG, which significantly reduces graph size (e.g., climinating 99% of nodes in the WebQSP dataset) and speeds up running time.
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Table 12: Performance and Efficiency of Various Methods on the WebQSP dataset.
Setting
Method
Hit@1
Time
Question only
61.16
31 min
Inference-only
Textual graph and question
41.06
40 min
Prompt Tuning
48.34
18.7 min/epoch
Frozen LLM w/ PT
G-Retriever
70.49
6.2 min/epoch
LoRA
66.03
19 min/epoch
Tuned LLM
G-Retriever w/ LoRA
73.79
6.9 min/epoch
F Discussion on Explainability
We believe G-Retriever enhances explainability in the following ways:
Retrieved subgraph. By returning the most relevant subgraph in response to a query, users can see which parts of the graph are considered important for the answer. This helps users understand the basis of the model's 's responses. For example, users want to understand why certain information is present absent in the LLM'' response, they inspect the subgraph to whether such information is present or absent in the retrieved subgraph.
Conversational Interface. G-Retriever allows users to ask follow-up questions and receive detailed natural language explanations. For example, if a user questions the LLM's response, they can ask, "Why do you think [xxx]? Please explain your answer." This interactive capability enables users to explore the model's 's reasoning process and gain deeper insights into how interprets graph data.
G Hallucination in Graph LLMs
In this section, we present quantitative results regarding hallucinations in the SceneGraphs dataset.
Baseline. For our baseline, we adapted MiniGPT-4 [57] to graph contexts. This approach involves a frozen LLM interacting with trainable GNN that encodes graph data soft prompt, denoted as LLM+Graph Prompt Tuning. We focus on graph prompt tuning as the baseline, instead of converting the graph into text, since the textual representation of the graph is large and consistently exceeds the input token limits of LLMs.
Experiment Design, We instructed the LLM to answer graph-related questions and to list nodes or edges in the explanation graph that support its answers. Since standard answers for these questions do not exist, allowing the LLM respond flexibly, it becomes challenging evaluate its responses. To address this, we manually examined 100 responses generated by our method and the LLM with graph prompt tuning, verifying whether the nodes and edges referenced in the LLM's output actually exist in the graph.
Evaluation Metrics. We assessed the model's faithfulness using three metrics: the fraction of valid nodes (denoted as Valid Nodes), the fraction valid edges (denoted as Valid Edges), and the fraction of times the entire set of nodes and edges cited was valid (denoted as Fully Valid Graphs).
Results. The results, as depicted in Table illustrate the comparative effectiveness of the G-Retriever over the baseline LLM+Graph Prompt Tuning method in reducing hallucinations. The LLM+Graph Prompt Tuning approach demonstrated a significantly lower accuracy in referencing graph elements, with only 31 % of nodes and 12% of edges being valid, and the entire set of nodes and edges being valid only 8% of the time. contrast, G-Retriever showed substantial improvements: 77% validity in nodes, 76% in edges, and 62% in the overall validity of referenced node-edge sets. These results underscore the significant reduction hallucinations with G-Retriever, particularly in the challenging task of accurately citing both nodes and edges in graph-based contexts.
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H Demonstrations
We demonstrate the interaction capabilities of G-Retriever with creative questions on different datasets: ExplaGraphs, SceneGraphs, and WebQSP. These examples are showcased in Tables 13
14 and 15 respectively. Additionally, the examples are visualized in Figure [
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Table 13: Example of creative chat on Expl ExplaGraphs.
Graph
node id,node attr 0.human 1.commetic surgery
2. 2.women
3,change appearance
4,addiction
5,searching for perfection 6,stop
7.positive
sre,edge_attr,dst
0.receives action,1
2.part of,0
L,sedd 3
3,capable of.4
4,created by.5
4,not capable of,6
4,is not a,7
User
Write 500-word argument essay based the information provided in the explanation graph.
Model
The explanation graph provides detailed analysis of the reasons why women undergo cosmetic surgery. According to the graph, the main reasons include the desire to change one's appearance, the influence of societal beauty standards, and the addiction to searching for perfection. These reasons are further supported hy the various nodes and edges in the graph, which provide additional insights into the motivations and consequences of cosmetic surgery.
Firstly, the node labeled "human" highlights the fundamental human desire feel attractive and confident in one's appearance. This desire is deeply ingrained in our culture, where beauty often associated with happiness, success, and social acceptance. As a result, many women feel pressure to conform to societal beauty standards, which can lead to feelings of insecurity and low self- self-esteem. In response to these feelings, some women may turn cosmetic surgery as means of improving their appearance and boosting their self-confidence.
Secondly, the node labeled "women" highlights the gendered nature of cosmetic surgery. While men also undergo cosmetic surgery, the majority of cosmetic surgery patients are women. This likely due to the fact that women more likely to he exposed to societal beauty standards and are more likely to internalize these standards. As result, women may he more likely to feel insecure about their appearance and more likely to seek cosmetic surgery as a means of improving their looks.
Thirdly, the node labeled "addiction" highlights the potential negative consequences of cosmetic surgery. While some women may initially feel improved confidence and self-esteem after un- dergoing cosmetic surgery, the graph suggests that this initial boost may last. In fact, some women may become addicted to the constant pursuit of perfection, leading to a cycle of surgery and dissatisfaction. This can have serious psychological and emotional consequences, including depression, anxiety, and a loss of self-esteem.
Fourthly, the node labeled "searching for perfection" highlights the unrealistic expectations that many women have about the results cosmetic surgery. The graph suggests that women may have unrealistic expectations about the ability of cosmetic surgery to improve their appearance and boost their self-esteem. This can lead to disappointment and dissatisfaction with the results of surgery, as well continued pursuit of perfection through additional surgery.
Finally, the node laheled "stop" highlights the importance of critical thinking and self-reflection when comes to cosmetic surgery. The graph suggests that women should be aware the potential negative consequences of cosmetic surgery and should carefully consider the reasons why they are undergoing surgery. They should also be aware of the unrealistic expectations that they may have about the results of surgery and should be prepared to address any negative emotions feelings of dissatisfaction that may arise.
In conclusion, the explanation graph provides a detailed analysis of the reasons why women undergo cosmetic surgery. The graph highlights the fundamental human desire to feel attractive and confident in onc's appearance, the gendered nature of cosmetic surgery, the potential negative consequences of cosmetic surgery, and the importance of critical thinking and self-reflection when it comes to surgery. By understanding these reasons and the potential consequences of cosmetic surgery, women can make informed decisions about whether or not to undergo surgery and can work to address any negative emotions or feelings of dissatisfaction that may arise.
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Table 14: Example of creative chat on SceneGraphs. We utilize GPT-4 generate an image from the graph, assisting readers in visualizing it
Graph
bode__d,nnoe_atte attr
0,name: head; attrihute: black; (x.y,w,h): (386, 185, 8, 4)
1,name: swimming pool; attribute: small, water; (x.ywwh):: (465, 92, 35, 3)
2,name: shore; (x.y,w,h): (49, 85, 436, 108)
3,name: fence; attribute: wired; (x.y.w.h): (11, 158, 167, 46)
4.name: mountains; attribute: covered: (x.y.w.h): (393, 53, 107, 12)
5,name: water; attribute: blue; (x.y.w.h): (0, 50, 499, 154)
6,name: sheep; attribute: walking, grazing; (x,yw,h): (243, 191, 20, 13)
7,name: sky; attribute: blue, clear, cloudy; (x,y,w,h): (0, 0, 501, 60)
8.aame: sheep; attribute: white, fluffyy (x.yw,h): (409, 176, 13, 10)
9.name: field; attribute: large, green, grassy; (x.y,w,h); (0, 138, 503, 112)
10,name: grass; (x.y.w,h): (0, 84, 500, 166)
11.name: sheep; attribute: white, fluffy; (x.ywwh);; (398, 178, 13, 11)
12.name: sheep: attribute: white, fluffy; (xyyw.h): (385, 179, 17, 11)
13.amme: sheep; attribute: white, fluffyy (x.yw.h):: (364, 181, 14, 10)
14.name: shore; attribute: sandy; (x.y.wh)): (190, 123, 171, 12)
sre, edge_atte _at__attr,a dst
0,to the left of, 1'n 0,to the right of, 13 3 left of,6in 6,t0 the right 1 6.grazing on, 10kn 6,in,9'n 6,in, 10kn 7,above,5in c,catin 10 8,to the right of, I'n 8, the right of, 1f,n 11,to the left of,8'n 11,10 the right of.0'n 11, the right of, 11to the right of,12 11 eeating 10'n 12.eating. 10tn 12,t0 the left of,8'n 12,t0 the right of, 12,to the left of, I'm 13,t0 the left of,0'n 13,10 the left of, P'n 13,10 left 12.12 13,eating.10.r
Image
User
Write an 500-word advertisement for this place in the scene graph that would make people want to visit it.
Model
Here's an example of a 500-word advertisement for the place based on the scene graph: "Experience the breathtaking beauty of place, where the rolling hills meet the sparkling water of the swimming pool. Take leisurely stroll through the lush green fields, and let the gentle breeze caress your face as you gaze out at the picturesque view. The sheep graze peacefully in the distance, their white coats glistening in the sunlight. The sky is brilliant blue, with fluffy white clouds drifting lazily across. Come and bask in the serene ambiance of our place, where the natural beauty will leave you in awe."
Sure!
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Table 15: Example of creative conversation on the WebQSP dataset.
node id, node attr
8, benjamin franklin
63, glass harmonica
98. lightming rod
99, stealing god's 's thunder: benjamin franklin's lightning rod and invention of america
227, benjamin franklin worked with thomas jefferson
442, franklin stove
525, bifocals
706, benjamin franklin drawing electricity from the sky
1044, prokop diviš
stc, edge_attr, dst
706, aarual_artartwwoll artt_artttuuu subject,
Graph
8, basbbbse berobaaee.wtoppie www.www.cooo..ttce connections from, 227
8, book.book book.book_subeect..ooo
99
525, iwtiiveention.iveentee 8
442, www.invention.invenn
98. www.invention.nvec.. inventor, 1044
8, fnw.inventoriineeniios. 98
8, crnventioo.inventan 8
63, law.
htgp:/mmmmmmassmmooattrrrrrnnni i_deas,ddea,,_e 442
98, www.invention.inve. inventor, 8
8,
525
442, base. guaseaargumenttaas.orrrggg idea.innovator,
8, daw.inventorinventiinn 442
8, daw.inventor.iveention,, 63
Question: what else did ben franklin invent? Surrounding this question, develop educational tour through the knowledge graph, highlighting interesting facts and connections in an engaging way.
User
Great! Based the provided knowledge graph, here's educational tour that highlights interesting facts and connections related to Benjamin Franklin's inventions and other notable achievements:
1. Franklin's 's Lightning Rod: Start by exploring Franklin's 's most famous invention the lightning rod. Discover how Franklin's 's invention protected buildings from lightning strikes and how it changed the way people understood electricity. Connection: Franklin worked with Thomas Jefferson on the lightning rod, and their collaboration led to the development of the concept of positive and negative charges.
2. Bifocals: Move on to Franklin's invention of bifocal glasses. Learn how Franklin's vision problems inspired him to create a new type of glasses that allowed people to see both near and far objects clearly. Connection: Franklin's 's bifocal glasses were a groundbreaking invention that improved the lives of millions of people around the world.
3. Franklin Stove: Explore Franklin's 's design of the Franklin stove, cast-iron stove that provided more heat with less fuel. Discover how Franklin's is stove revolutionized heating systems and how it helped to reduce fuel costs for houscholds and businesses. Connection: Franklin's 's stove was an carly example of energy efficiency and sustainability, and paved the way for modern heating systems.
4. Prokop Diviš: Learn about Franklin's 's connection to Prokop Diviš, Czech inventor who built on Franklin's 's work and developed the first electric generator. Discover how Diviš's invention helped power the Industrial Revolution and how it changed the way people lived and worked. Connection: Franklin's work on the lightning rod inspired Diviš explore the properties of electricity, and their collaborative efforts helped to advance the field of electrical engineering. 5. Innovator and Original Ideas: Explore Franklin's 's reputation as an innovator and his ability to come with original ideas. Discover how Franklin's 's creativity and curiosity led to many of his inventions and how he was able see connections between seemingly unrelated things. Connection: Franklin's innovative spirit and ability think outside the box inspired many other inventors and scientists, including Thomas Fdison and Nikola Tesla.
Model
6. Collaboration and Networking: Highlight Franklin's 's collahorative approach to invention and his ability to network with other inventors and scientists. Discover how Franklin's 's connections with other innovators helped to advance the field of science and technology. Connection: Franklin's collaborations with other inventors and scientists. such Thomas Jefferson and Prokop Divis, helped accelerate the pace of innovation and pave the way for future breakthroughs.
7. Legacy and Impact: Reflect on Franklin's 's lasting legacy and the impact of his inventions on modern society. Discover how Franklin's 's inventions continue to inspire new generations of inventors and scientists and how they have helped to shape the world we live today. Connection: Franklin's inventions and ideas have had lasting impact many fields, including seienee, technology, and society whole. His legacy continues to inspire and motivate people around the world.
By following this educational tour through the knowledge graph, students will gain deeper understanding of Benjamin Franklin's 's inventions and their impact on modern society. They will also learn about Franklin's 's collaborative approach to invention, his innovative spirit, and his ability to think outside the box.
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Reviews:
[{"review_id": "eGjU8XlX2V", "reviewer": "Reviewer_GHV4", "summary": "The work builds a new GraphQA benchmark for real-world graph question answering and presents G-Retriever, an architecture adept at complex and creative queries. Given a query and a graph, G-Retriever retrieves a connected subgraph from the original graph according to the query. The subgraph,  a textualized graph, and the query are fed into an architecture consisting of a graph encoder aligned with an LLM. Experiments show that G-Retriever achieves SoTA in textual graph tasks across multiple domains, significantly improves efficiency, and demonstrates resistance to hallucination.", "questions": "1. After the subgraph is retrieved, is it possible to use neuro-symbolic methods (e.g. answer set programming or graph search) instead of using a graph encoder together with an LLM? It would be more efficient and cost much less.", "limitations": "Yes", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The idea of incorporating retrieval for graph question answering is novel and interesting and could be inspiring for future works.\n2. The proposed G-retriever framework performs well on the GraphQA dataset. The retrieval method scales effectively with larger graph sizes, which is not addressed in many previous works. Locating the related subgraph instead of using the entire graph also reduces hallucination. \n3. The experiments are comprehensive, including analysis from various aspects such as hallucination and graph encoder selection.", "weaknesses": "1. The framework seems to mainly work for larger graphs. For tasks with smaller graphs, such as the ExplaGraphs dataset with an average node number of  5.17, it is unnecessary to use such a framework. The performances of G-retrieval and GraphToken are nearly the same for the ExplaGraphs dataset.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer very much for the careful reading and comments regarding our work. Please, see below our answer to the raised comments/questions.\n\n> **Reviewer:** The framework seems to mainly work for larger graphs. For tasks with smaller graphs, such as the ExplaGraphs dataset with an average node number of 5.17, it is unnecessary to use such a framework. The performances of G-retrieval and GraphToken are nearly the same for the ExplaGraphs dataset.\n\n**Authors:** We acknowledge that G-Retriever and GraphToken demonstrate similar performance on the ExplaGraphs dataset, which consists of small graphs. As noted by the reviewer, other techniques like GraphToken can be employed effectively on this dataset. However, our aim in applying G-Retriever to ExplaGraphs dataset was to highlight the flexibility and effectiveness of the proposed technique across a wide range of graph sizes. This versatility is particularly advantageous in datasets like SceneGraphs, where graph sizes, although small (averaging 19.1 nodes) and varying significantly (ranging from 1 to 126 nodes with a standard deviation of 8.3). For these small-scale graphs, G-Retriever significantly outperforms GraphToken, achieving 0.8131 accuracy compared to 0.4903, as reported in Table 5. Additionally, G-Retriever offers extra benefits, such as a flexible conversational interface. As shown in Figure 1, G-Retriever can generate an essay based on a given explanation graph. We believe that this versatile question-answering capability adds substantial value beyond just accuracy.\n\n\n|     | # node: min, max, mean ± std | # edge: min, max, mean ± std|\n| -------- | ------- | ------- |\n| ExplaGraphs  | 4, 9, 5.17 ± 1.18    | 3, 8, 4.25 ± 1.23 |\n| SceneGraphs | 1, 126, 19.13 ± 8.28     | 0, 1657, 68.44 ± 65.77 |\n| WebQSP    | 3, 2000, 1371.18 ± 566.91    | 2, 10818, 4253.27 ± 2238.12 |\n\n\n\n> **Reviewer:** After the subgraph is retrieved, is it possible to use neuro-symbolic methods (e.g. answer set programming or graph search) instead of using a graph encoder together with an LLM? It would be more efficient and cost much less.\n\n**Authors:** Thank you for the insightful suggestion. While neuro-symbolic methods such as answer set programming or graph search can indeed offer efficiency and cost-effectiveness, they generally require a predefined specification of patterns, i.e. symbols, to guide the search process. In our graph QA scenario, the patterns are highly dependent on the query, which can vary significantly in complexity, making it challenging to effectively predefine these patterns. However, we acknowledge that for specific applications where patterns can be clearly defined, neuro-symbolic approaches could be a viable alternative and are certainly worth exploring in future work.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3CIs1UZ3BI", "reviewer": "Reviewer_mCtm", "summary": "This paper proposed a Graph Question Answering (GraphQA) benchmark with data collected from different tasks including ExplaGraphs, SceneGraphs, and WebQSP. Then, they proposed G-Retriever method, introducing the first retrieval-augmented generation (RAG) approach for general textual graphs, which can be fine-tuned to enhance graph understanding via soft prompting. G-Retriever performs RAG over a graph by formulating this task as a Prize-Collecting Steiner Tree optimization problem. The generation model is fine-tuned by a graph token and the textualized graph. Empirical evaluations show G-Retriever outperforms baselines on textual graph tasks from multiple domains, scales well with larger graph sizes.", "questions": "n/a", "limitations": "n/a", "rating": 7, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "- This proposed GraphQA benchmark is comprehensive and comes in a timely manner. \n- Converting the problem of finding a connected subgraph that maximizes the total prize values of its nodes while minimizing the total costs of its edges to PCST is smart.\n- Strong experimental results by using the proposed G-retriever on all three datasets.", "weaknesses": "I don't see major weaknesses of this paper. Several minor points:\n- I think several baselines are missing: (1) Simply feed the retrieved top-k nodes/edges (plus its neighbors) to the LLM would be able to show the effectiveness of PCST. (2) It would be helpful to understand the generation model by replacing the tuned generation model to a fixed LLM like GPT4, Claude, etc.\n- When encoding the nodes and edges, the context of the nodes/edges is missing, it could be beneficial to include the context (e.g., adjacent nodes)\n- Table captions should be more informative so that they are easier to understand (e.g., In Table5, what metrics are used?)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We would like thank the reviewer very much for the careful reading and comments regarding our work. Please, see below our answer to the raised comments/questions.\n\n> **Reviewer:** I think several baselines are missing: (1) Simply feed the retrieved top-k nodes/edges (plus its neighbors) to the LLM would be able to show the effectiveness of PCST.\n\n**Authors:** To demonstrate the effectiveness of PCST, we compared it to a similar baseline, KAPING [1]. KAPING retrieves the top-k triples related to the question from the graph, adds them to the input question as a prompt, and then sends this to LLMs to generate the answer. \nIn addition to KAPING, we included the following baseline methods to address the reviewer's concern:\n- Top-k nodes plus their neighbors: For each query, For each query, the top-k nodes and their one-hop neighbors are retrieved.\n- Shortest path retrieval: This approach involves retrieving the top-k nodes and the shortest paths between them.\nFor all methods, we set k = 5 and used llama2-7b-chat as the LLM. The results are presented in the table below, where we observed that our PCST-based retrieval outperforms the baseline retrieval methods, achieving an accuracy of 66.17 on the WebQSP dataset.\n\n| Method                               | Hit@1 |\n|--------------------------------------|--------------------|\n| PCST retrieval                        | 66.17              |\n| top-k triples retrieval (KAPING)      | 52.64              |\n| top-k nodes plus its neighbors        | 49.82              |\n| shortest path retrieval               | 55.20              |\n\n> **Reviewer:** (2) It would be helpful to understand the generation model by replacing the tuned generation model to a fixed LLM like GPT4, Claude, etc.\n\n**Authors:** To address this concern, we conducted additional experiments by replacing the tuned generation model with fixed LLMs on the WebQSP dataset. Specifically, we first applied the PCST retrieval to obtain the subgraph, then converted it into text and fed it into the fixed LLMs along with the query. We considered two fixed LLMs: the open-source llama2-7b-chat and the closed-source GPT-4. The results are summarized in the table below:\n|  Method                   | Hit@1 |\n|---------------------|--------------------|\n| llama2-7b-chat      | 66.17              |\n| GPT-4o              | 67.87              |\n| G-Retriever | 70.49              |\n\nAs the results indicate, G-Retriever outperforms both fixed LLM baselines. This demonstrates the effectiveness of our approach in the tuned generation model.\n\n> **Reviewer:** When encoding the nodes and edges, the context of the nodes/edges is missing, it could be beneficial to include the context (e.g., adjacent nodes)\n\n**Authors:** Thank you for your observation. In our indexing step, we use a pre-trained language model (LM) to encode the text attributes associated with each node and edge into embeddings. Although we do not explicitly include the context, such as adjacent nodes, during this initial encoding, we address this in the generation step. Specifically, our framework incorporates a graph neural network (GNN) component designed to aggregate information from adjacent nodes and update the node and edge representations based on the graph context. This approach allows us to effectively capture and use the context during the generation phase. Therefore, while the context is not directly included in the initial encoding, it is integrated later in the process through the GNN, ensuring that the contextual information is considered in the overall framework.\n\n> **Reviewer:** Table captions should be more informative so that they are easier to understand (e.g., In Table5, what metrics are used?)\n\n**Authors:** Thank you for the suggestion. In Table 5, we assessed the model’s faithfulness using three metrics: the fraction of valid nodes (denoted as Valid Nodes), the fraction of valid edges (denoted as Valid Edges), and the fraction of times the entire set of nodes and edges cited was valid (denoted as Fully Valid Graphs). As recommended, we will update the table captions in our revised manuscript to include this information.\n\n\n\n**Reference**\n\n[1] Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering, 2023.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "LfmxQs7lpZ", "reviewer": "Reviewer_GQPK", "summary": "This paper proposes a retrieval-augmented method G-Retriever for graphs with textual attributes. It introduces a graph question answering (GraphQA) benchmark by converting existing graph datasets into uniform format. Subsequently, it proposes a G-Retriever method to answer questions related to the textual graphs. Specifically, it first retrieves top-k most relevant nodes and edges from the textual graphs and then constructs a subgraph based on the retrieved nodes and edges using an existing algorithm called Prize-Collecting Steiner Tree (PCST). Subsequently, G-Retriever leverages an GAT model to obtain the pooled representation of the constructed subgraph, which is used as a soft prompt for an LLM to generate the answer.", "questions": "1. What does “chat with their graph” mean and how can the proposed G-Retriever model achieve this purpose?\n\n2. How can the proposed G-Retriever improve the explainability? Are there any qualitative analyses to support this argument?\n\n3. What are the advantages of using the PCST algorithm to retrieve subgraphs?\n\n4. How to evaluate the quality of the retrieved subgraphs?", "limitations": "The authors have discussed the limitations of their work in the paper.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The idea of retrieving subgraphs from textual graphs to augment the LLM is interesting. Experimental results on three datasets in the GraphQA benchmark demonstrate the effectiveness of the proposed G-Retriever model. The code is provided for reproducibility.", "weaknesses": "1. The paper mentions “chat with their graph” in the abstract and the introduction. However, it is not very clear what this concept means. Additionally, after the abstract and introduction, there are no further introductions or explanations about this concept.\n\n2. In line 69, the paper states that the proposed G-Retriever can improve the explainability. However, there are no empirical analyses regarding the explainability of the proposed method. \n\n3. The novelty of the proposed GraphQA benchmark seems limited, as it only converts three existing graph datasets to a uniform format. \n\n4. I think the main novelty of the proposed G-Retriever model is that it leverages a PCST algorithm to find a connected subgraph between the retrieved nodes and edges. However, the paper does not clearly explain the advantages of retrieving a subgraph. Why is it necessary to retrieve a subgraph rather than directly using the texts of the retrieved nodes and edges as inputs for the LLM? Constructing a subgraph could potentially introduce noises, which might negatively impact the performance. Additionally, the paper does not adequately justify the use of PCST for finding the subgraph. Have the authors explored other alternative methods, such as the shortest paths between the retrieved nodes?\n\n5. There are no quantitative or qualitative analyses regarding the quality of the retrieved subgraphs. Providing such analyses could offer some insights into the effectiveness of the subgraphs. \n\n6. In the efficiency evaluation (section 6.3), the paper only compares the training times of G-Retriever and its variant without retrieval (I assume this is what “Before Retrieval” means, as there are no explanations about the columns in Table 4). However, it would be more appropriate to compare the inference times of G-Retriever and its variant during the inference stage. G-Retriever requires additional steps of retrieving nodes and edges, as well as constructing subgraphs using the PCST algorithm, which may result in longer inference times compared to not using retrieval.\n\n7. A lot of necessary experimental details in section 6.4 and 6.5 are provided in the Appendix, which may hinder the readers’ understanding of the results and analyses in these sections. For instance, it is impossible to understand what “Valid Nodes”, “Valid Edges” and “Fully Valid Graphs” mean in Table 5 without referring to the Appendix. Additionally, in section 6.4, the paper uses manual examination to evaluate the hallucination performance of LLMs. However, it is unclear how many annotators were involved in this process and whether there is a potential for biases during the evaluation. \n\n8. The texts in Figure 1 and Figure 2 are too small to read.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We wish to thank the reviewer very much for the careful reading and comments regarding our work. Please, see below our answer to the raised comments/questions.\n\n> **Reviewer (W1 & Q1):** Explanation on “chat with their graph”\n\n**Authors:** By “chat with their graph,” we mean that users can interact with the graph through a conversational interface. Users can input a textual graph and pose natural language queries, to which G-Retriever will respond in natural language. For example, as shown in Figure 1, if a user provides a mindmap-like explanation graph and requests an argument essay, G-Retriever can generate the essay accordingly. This feature enhances human-AI interaction, making the model more intuitive and aligned with human expectations. We will clarify and elaborate on this concept in the main sections of our revised manuscript.\n\n> **Reviewer (W2 & Q2):** Explainability of G-Retriever\n\n**Authors:** We believe G-Retriever enhances explainability in the following ways: \n- **Retrieved subgraph.** By returning the most relevant subgraph in response to a query, users can see which parts of the graph are considered important for the answer. This helps users understand the basis of the model’s responses. For example, if users want to understand why certain information is present or absent in the LLM’s response, they can inspect the subgraph to see whether such information is present or absent in the retrieved subgraph.\n- **Conversational Interface.** G-Retriever allows users to ask follow-up questions and receive detailed natural language explanations. For example, if a user questions the LLM’s response, they can ask, “Why do you think [xxx]? Please explain your answer.” This interactive capability enables users to explore the model’s reasoning process and gain deeper insights into how it interprets graph data.\n\nWe will include specific examples in the revised version of our manuscript to illustrate the two explainability properties mentioned above.\n\n> **Reviewer (W3):** Novelty of GraphQA benchmark\n\n**Authors:** Due to the character limit, we kindly refer the reviewer to our general response “G1: Contribution of the GraphQA Benchmark”, where we have addressed this question in detail.\n\n> **Reviewer (W4 & Q3):** PCST and alternative retrieval methods\n\n**Authors:** Due to the character limit, we kindly refer the reviewer to our general response “G2: Elaboration on PCST-Based Retrieval”, where we have responded to this question in detail.\n\n> **Retriever (W5 & Q4):** Quality of the retrieved subgraphs\n\n**Authors:** We quantify the quality of our retrieval method as follows: \n\nWe examine the retrieval subgraph; if the label is contained within it, we consider it a successful retrieval. We calculate the retrieval success rate of our method and the retrieval method proposed in KAPING [1] on the WebQSP dataset. The results are as follows:\n- Hit@1 accuracy for [KAPING with top-k triple retrieval] is 60.81%.\n- Hit@1 accuracy for [Ours with PCST-based subgraph retrieval] is 67.87%.\n\nThis demonstrates the effectiveness of our method.\n\n\n> **Reviewer (W6):** Efficiency evaluation\n\n**Authors:** We conducted additional experiments on the WebQSP dataset comparing inference times of G-Retriever with and without retrieval:\n| Method                       | Time in minutes | Accuray with Hit@1  |\n|------------------------------|----------|--------|\n| G-Retriever                  | 9.55     | 70.49  |\n| G-Retriever (w/o retrieval)  | 21.01    | 63.84  |\n\n\nDespite additional steps, G-Retriever is faster (9.55 vs. 21.01 minutes) and more accurate (70.49% vs. 63.84%). The speedup is due to:\n\n- PCST subgraph construction is very efficient. For instance, in the WebQSP dataset, even the largest subgraph (2,000 nodes, 6,104 edges) takes only 0.29 seconds to construct.\n- PCST retrieval reduces the graph size significantly, eliminating up to 99% of nodes in the WebQSP dataset, which in turn speeds up inference time.\n\n\n\n> **Reviewer (W7):** Experimental details\n\n**Authors:** In Table 5, we assessed the model’s faithfulness using three metrics: the fraction of valid nodes (denoted as Valid Nodes), the fraction of valid edges (denoted as Valid Edges), and the fraction of times the entire set of nodes and edges cited was valid (denoted as Fully Valid Graphs). As recommended, we will update the table captions in our revised manuscript to include this information.\n\nRegarding the hallucination evaluation in section 6.4, the manual examination was conducted by the authors. We will develop the following points:\n\n**1. Necessity of Manual Evaluation:** Since standard answers are not available for these questions, the LLM’s responses are inherently flexible. This flexibility, along with the varied output formatting, makes automatic evaluation difficult, necessitating manual review.\n\n**2. Objectivity in Determining Hallucinations:** The determination of hallucinations is based on objective criteria. For example, in Table 1, in the response from “LLM w/ Graph Prompt Tuning,” it is stated: “ The animal in the bushes is a deer. Nodes: * Deer (node 1), * Bushes (node 2); Edges: * Deer → Bushes (edge 1) * Deer → Grass (edge 2) * Bushes → Grass (edge 3)”. The fraction of “valid nodes” is 1/2 = 0.5, as “Bushes” exists in the scene graph while “Deer” does not. The fraction of “valid edges” is 0/3 = 0, since none of the mentioned edges exist in the scene graph. As there are hallucinated nodes and edges, this is not a “fully valid graph.”\n\n**3. Transparency and Bias Mitigation:** We recognize the importance of transparency and minimizing bias in our evaluation process. To address potential concerns, we will open-source the model outputs used for these manual checks, allowing for public scrutiny and verification.\n\n> **Reviewer (W8):** The texts in Figure 1 and Figure 2 are too small to read.\n\n**Authors:** We will enlarge the text in Figures 1 and Figure 2 to enhance readability in our next manuscript.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "L0jbCPm40U", "reviewer": "Reviewer_RHZZ", "summary": "This paper introduces G-Retriever, which combines LLMs, GNNs and RAG for graph question-answering tasks. The authors first develop a more comprehensive benchmark named GraphQA. Then they present G-Retriever, which has four main steps including indexing, retrieval, subgraph construction and answer generation. Specifically, G-Retriever use an adapted PCST method, which also consider the importance of the edge semantics. The constructed subgraph and the query are then sent to the LLM for final answer generation. Extensive experiments validate the effectiveness and efficiency of G-Retriever.", "questions": "Please see the questions in the Weakness section.", "limitations": "The authors have discussed the limitations of their work in the paper.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The introduction of the GraphQA benchmark fills a significant gap in the research community by providing a comprehensive benchmark for evaluating graph QA applications.\n2. The motivation of the proposed G-Retriever is clear and the paper is well-structured. \n3. Extensive experimental results demonstrate that G-Retriever can consistently outperform  baseline models.\n4. Codes are provided for reproducibility.", "weaknesses": "- In Section 5.1 (Indexing), the authors use a pre-trained LM to encode the text attributes of nodes and edges into representations. However, there is no ablation study to demonstrate the importance of the node attributes or the text attributes, which leaves an unexplored gap in understanding the contribution of these features to the overall performance of the G-Retriever.\n- In Section 5.3 (Subgraph Construction), the authors use PCST to obtain a more refined subgraph. However, the motivation for selecting PCST is not clearly explained. Is PCST the optimal for this task? How the \"optimally sized and relevant subgraphs\" are defined? At least the authors could consider to justify this by comparing some naive approaches, such as fixed number or fixed similarity threshold.\n- The claim of the new benchmark is somewhat weak, as it primarily involves the reintroduction of an existing dataset. Additionally, the contributions related to the QA formulation and graph reformatting are limited.\n- The evaluation of hallucinations lacks sufficient detail and requires further elaboration.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer very much for the careful reading and comments regarding our work. Please, see below our answer to the raised comments/questions.\n\n> **Reviewer:** In Section 5.1 (Indexing), the authors use a pre-trained LM to encode the text attributes of nodes and edges into representations. However, there is no ablation study to demonstrate the importance of the node attributes or the text attributes, which leaves an unexplored gap in understanding the contribution of these features to the overall performance of the G-Retriever.\n\n**Authors:** The text attributes in G-Retriever play a critical role in three main areas: (1) Retrieval: Node and edge embeddings are used to select the top-k nodes and edges based on their semantic similarity to the query embedding. (2) GNN Input: These embeddings are fed into the GNN, which processes the graph structure and refines the embeddings to capture more complex relationships within the graph. (3) LLM Input: The retrieved subgraph is textualized and used as input for the LLM to generate answer.\n\nTo address the reviewer’s concern, we have conducted an ablation study to assess the contribution of these features.\n\n| Features | Without Node | Without Edge |\n|----------|--------------|--------------|\n| Retrieval | 66.58 | 58.37 |\n| GNN Input | 68.85 | 67.87 |\n| LLM Input | 56.32 | 68.24 |\n\n\n> **Reviewer:** In Section 5.3 (Subgraph Construction), the authors use PCST to obtain a more refined subgraph. However, the motivation for selecting PCST is not clearly explained. Is PCST the optimal for this task? How the \"optimally sized and relevant subgraphs\" are defined? At least the authors could consider to justify this by comparing some naive approaches, such as fixed number or fixed similarity threshold.\n\n**Authors:** Due to the character limit, we kindly refer the reviewer to our general response “G2: Elaboration on PCST-Based Retrieval”, where we have addressed this question in detail.\n\n> **Reviewer:** The claim of the new benchmark is somewhat weak, as it primarily involves the reintroduction of an existing dataset. Additionally, the contributions related to the QA formulation and graph reformatting are limited.\n\n**Authors:** Due to the character limit, we kindly refer the reviewer to our general response “G1. Contribution of the GraphQA Benchmark”, where we have addressed this question in detail.\n\n\n> **Reviewers:** The evaluation of hallucinations lacks sufficient detail and requires further elaboration.\n\n**Authors:** Thank you for your feedback. Due to space constraints, the detailed evaluation of hallucinations is provided in Appendix F. Here, we offer a summary:\n\n- **Experiment Design.** We instructed the LLM to answer graph-related questions and list the nodes or edges in the explanation graph that support its answers. Since there are no standard answers, evaluating the LLM’s responses becomes challenging. To address this, we manually examined 100 responses generated by both our method and a baseline method (LLM with graph prompt tuning) to verify whether the referenced nodes and edges actually exist in the graph.\n- **Evaluation Metrics.** We assessed the model’s faithfulness using three metrics: the fraction of valid nodes (Valid Nodes), the fraction of valid edges (Valid Edges), and the fraction of times the entire set of nodes and edges cited was valid (Fully Valid Graphs).\n- **Baseline.** We adapted MiniGPT-4 [57] to graph contexts as our baseline, using a frozen LLM with a trainable GNN that encodes graph data as a soft prompt (LLM+Graph Prompt Tuning). We focused on graph prompt tuning due to the large size of the textual representation of the graph, which often exceeds the input token limits of LLMs.\n- **Results.** As shown in Table 5, G-Retriever outperforms the baseline in reducing hallucinations. The baseline method showed only 31% valid nodes, 12% valid edges, and 8% fully valid node-edge sets. In contrast, G-Retriever achieved 77% validity in nodes, 76% in edges, and 62% overall validity in node-edge sets. These results highlight the effectiveness of G-Retriever in accurately referencing graph elements and significantly reducing hallucinations.\n\nWe will ensure that these points are clearly elaborated upon in the revised manuscript for better understanding.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
We sincerely thank the reviewers for their time and effort in evaluating our paper.

In this global response, we aim to clarify the contributions of the GraphQA benchmark (G1), elaborate on our PCST-based retrieval method (G2), and present new experiments conducted in response to the reviewers' feedback (G3).

**G1. Contribution of the GraphQA Benchmark:**
We acknowledge that the GraphQA benchmark involves converting three existing graph datasets into a uniform format. However, we believe this standardization provides significant value to the research community in several ways:

- **Task Introduction:** Unlike existing graph question-answering benchmarks that focus on small or synthetic graphs, our benchmark includes real-world applications and frames them as graph question-answering tasks.
- **Standardization:** A key and significant effort of this benchmark is the standardization and processing of diverse datasets into a uniform format suitable for GraphQA tasks. These datasets, previously used in different contexts, are redesigned to focus specifically on GraphQA, ensuring consistent and comparable evaluations across models.
- **Accessibility:** We have open-sourced the GraphQA benchmark, providing a unified format that simplifies model application across multiple datasets. This reduces the complexity of handling various data structures and preprocessing pipelines, lowering barriers for new researchers and encouraging broader participation. We have already seen several novel works using our GraphQA benchmark, and we expect rapid adoption within the LLM and GNN communities.
- **Baseline Comparisons:** The benchmark offers baseline performance metrics, helping researchers identify the strengths and weaknesses of new approaches compared to established baselines.

We will ensure these contributions are more clearly highlighted in the revised manuscript.

**G2. Elaboration on PCST-Based Retrieval:**

**1) Modeling motivation.** We formulate subgraph retrieval as a Prize-Collecting Steiner Tree (PCST) optimization problem. This is motivated by the need to find a connected subgraph containing most relevant nodes and edges, a goal that aligns well with the objectives of PCST: maximizing node values while minimizing edge costs. Though not universally acknowledged as optimal, we have empirically validated its effectiveness. 

**2) Effectiveness Compared to Other Retrieval Baselines.** To further demonstrate the effectiveness of PCST-based retrieval, we compared it to a similar baseline, KAPING [1]. KAPING retrieves the top-k triples related to the question from the graph, adds them to the input question as a prompt, and then sends this to LLMs to generate the answer. In addition to KAPING, we included the following baseline methods to address the reviewers' concern:

- Top-k nodes plus their neighbors: For each query, For each query, the top-k nodes and their one-hop neighbors are retrieved.
- Shortest path retrieval: This approach involves retrieving the top-k nodes and the shortest paths between them.

For all methods, we set k = 5 and used llama2-7b-chat as the LLM. The results are presented in the table below, where we observed that our PCST-based retrieval outperforms the baseline retrieval methods, achieving an accuracy of 66.17 on the WebQSP dataset.
| Method                               | Hit@1 |
|--------------------------------------|--------------------|
| PCST retrieval                        | 66.17              |
| top-k triples retrieval (KAPING)      | 52.64              |
| top-k nodes plus its neighbors        | 49.82              |
| shortest path retrieval               | 55.20              |



**3) Advantages of Subgraph-Based Retrieval.**
- **Context-Relevant.** Selecting nodes and edges in isolation may overlook neighborhood information. In contrast, PCST-based retrieval is guaranteed to return a connected subgraph, capturing the graph context during the retrieval process. This approach retrieves not only high-relevance nodes or edges but also “bridge” elements that connect these with contextually significant nodes or edges, which are crucial for generating a comprehensive response.
- **Size Management.** Compared to the shortest path method, PCST retrieval provides greater control over the size of the retrieved subgraph. By adjusting the prizes and costs on nodes and edges, users can fine-tune the subgraph's extent. In contrast, the shortest path approach lacks the ability to control the distance between the top-k nodes, which can lead to disconnected subgraphs or the inclusion of unnecessarily long paths.


**G3. New experiments:**

In response to reviewers' feedback, we conducted several new experiments to address their concerns:

- Comparison with more retrieval baselines, demonstrating the effectiveness of our PCST-based retrieval method (Table 16).
- Replacing the tuned generation model in G-Retriever with a fixed LLM (Table 17), highlighting the necessity of tuning the generation model, particularly through graph soft prompting in G-Retriever.
- Efficiency evaluation during the inference stage (Table 18), showing the significant efficiency gains brought by the retrieval process.
- Quantifying the quality of our retrieval method (Table 19) shows that our approach retrieves more accurate information than the KAPING baseline.
- Ablation study to demonstrate the importance of node and edge attributes (Table 20).

Please, find the new experiments in the newly rebuttal single-page PDF file.

We hope that our point-to-point responses with the new experiments have clarified the reviewers’ concerns. We are happy to answer any additional questions to clarify further.

Thank you again for your dedicated effort and time to review the paper.

Best regards,

The authors



**References**

[1] Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering, 2023.

Meta-review: This paper proposes a new method named G-Retriever, which is a retrieval-augmented generation (RAG) approach for general textual graphs, and develops a Graph Question Answering (GraphQA) benchmark with data collected from different tasks, such as scene graph understanding, common sense reasoning, and knowledge graph reasoning. The reviewers in general agree that the idea is novel and interesting, the experimental results are strong, and the compiled GraphQA benchmark is useful for the research community. There are some questions raised during the reviews which seem to have been addressed after rebuttal. The authors may revise the paper based on the discussions in the rebuttal.
Decision: Accept (poster)