InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignment (2024)

Abstract: Do current large language models (LLMs) better solve graph reasoning and generation tasks with parameter updates? In this paper, we propose InstructGraph, a framework that empowers LLMs with the abilities of graph reasoning and generation by instruction tuning and preference alignment. Specifically, we first propose a structured format verbalizer to unify all graph data into a universal code-like format, which can simply represent the graph without any external graph-specific encoders. Furthermore, a graph instruction tuning stage is introduced to guide LLMs in solving graph reasoning and generation tasks. Finally, we identify potential hallucination problems in graph tasks and sample negative instances for preference alignment, the target of which is to enhance the output’s reliability of the model. Extensive experiments across multiple graph-centric tasks exhibit that InstructGraph can achieve the best performance and outperform GPT-4 and LLaMA2 by more than 13% and 38%, respectively. 1.

text: Findings of the Association for Computational Linguistics ACL 2024, pages 13492–13510 August 11-16, 2024 ©2024 Association for Computational Linguistics
1 Introduction
Currently, large language models (LLMs) have succeeded in reasoning on textual data (Brown et al., 2020; Zhao et al., 2023c). However, there also exists rich information in graph data, that is difficult to represent using plain text (Jin et al., 2023), such as knowledge graphs (Schneider et al., 2022), symbolic graphs (Saba, 2023), and social networks (Wang et al., 2023d), etc.
To endow LLMs with the ability to solve graph tasks, a series of works focus on designing the interface (e.g., prompt engineering) of LLMs on graph data to make them understand the semantics without parameter optimization (Ye et al., 2023; Han et al., 2023; Zhang et al., 2023b; Zhang, 2023; Kim
* Work done during visiting at UC San Diego. † Corresponding Author. 1We have released the resource code in https://
github.com/wjn1996/InstructGraph.
et al., 2023; Jiang et al., 2023; Wang et al., 2023b; Luo et al., 2023), or injecting the graph embeddings into the partial parameters of LLMs through graph neural networks (GNNs) (Zhang et al., 2022; Chai et al., 2023; Tang et al., 2023; Perozzi et al., 2024). Despite significant progress, we explore these two challenges: 1) There still exists a semantic gap between graph and text, which may impede the LLM in graph reasoning and generation. 2) LLMs tend to generate hallucinations which may be caused by fabricated erroneous inputs or lack of pertinent knowledge. It can be viewed as the graph hallucination problem.
To overcome these challenges, we present a framework named InstructGraph that boosts LLMs by instruction tuning and preference alignment. A straightforward approach to solve the first challenge is to use a graph description (Ye et al., 2023) or graph embeddings (Chai et al., 2023), However, these methods require a large number of manual templates to describe the graph. Representing a large or complex graph via embeddings may cause information loss. In addition, the responses generated by the LLM with these methods are difficult to parse into actual graphs (Jin et al., 2023; Zhao et al., 2023c). Current investigations have demonstrated that LLMs have a great ability for code understanding and generation (Gao et al., 2023; Ma et al., 2023; Wong et al., 2023; Yang et al., 2024). Inspired by them, we can unify graph data into a code-like universal format to enhance the LLM’s understanding and generation performance on graph tasks. As shown in Figure 1, each graph can be converted into a code with basic variables, such as node_list (or entity_list), edge_list (or triple_list) and optional properties. To this end, a graph instruction tuning stage is introduced to train the LLM on these formulated data.
In addition, previous works have found that LLMs generate responses with hallucination when
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following the instructions, typically referring to fabricated erroneous inputs or lack of intrinsic knowledge (Zhang et al., 2023a; Ji et al., 2023). For example, the LLM may derive a wrong answer when being questioned on a graph that lacks key information, or the LLM may generate a graph with incorrect facts, conflicting, or missing information. However, how to reduce this effect in graph reasoning and generation is still under-explored. Hence, we introduce the graph preference alignment to alleviate the hallucination problem in the LLM’s reasoning and generation. Specifically, we follow the direct preference optimization (DPO) algorithm (Rafailov et al., 2023) to optimize the LLM to make better preferences. To automatically sample the negative instances in DPO, we explore various scenarios, such as unfactual graph, conflict graph and missing graph. , to simulate the graph hallucination problem.
To evaluate the effectiveness of our framework, we perform extensive experiments on multiple graph reasoning and generation tasks. Results reveal that the proposed InstructGraph achieves the best performance on both graph-centric instruction and preference tasks and outperforms the GPT4 (OpenAI, 2023) and LLaMA2 (Touvron et al., 2023b) by more than 13% and 38%, respectively.
2 Methodology
The skeleton is shown in Figure 2, which can be decomposed into three modules, i.e., graph input engineering, graph instruction tuning, and graph preference aligning.
2.1 Notation
Suppose that there are M graph tasks D = {D1, · · · DM}, and the corresponding dataset of each task can be denoted as Dj = {(Ii,Gi,Pi,Ai)}Nji=1, where Nj denotes the number of examples of Dj , Ii is the corresponding instruction 2, Gi = (Ei,Ri, Ti,Si) is the graph with one node (entity) set Ei, one optional relation set Ri, one edge (triple) set Ti, and one optional textual property set Si, Pi is the optional passage , and Ai is the final answer 3.
2.2 Graph Input Engineering
The first challenge is how to align the graph to the text to meet the sequence interface of LLMs, previous works solved this issue by using graph description (Ye et al., 2023) or embedding fusion method (Chai et al., 2023), which may make the
2We manually design the instruction for each dataset. 3Especially, the answer Ai can be not only an independent text but also one of Gi and Pi, depending on the task paradigm.
responses difficult to parse into actual graphs. Inspired by current LLMs that can simultaneously understand and generate code, we introduce a structured format verbalizing strategy to transform the graph into a simple code-like format. Formally, given one task graph Gi ∈ Dj , we denote M(·) as the structured format verbalizer, and the original graph can be mapped into a sequence as Ci = M(Gi). For the fundamental format, all nodes (or entities) are listed as a sequence with variable node_list (or entity_list), while all edges (or triples) are listed as a sequence with variable edge_list (or triple_list). For graphs that contain side information, we can simulate the object-oriented language to express the node (or entity) 4. Thus, we can unify all graphs into a unified format to align with textual data.
2.3 Graph Instruction Tuning
As shown in Figure 1, we first define four different groups of graph-centric instruction tasks to bol-
4Take the graph in Figure 1 as an example, the review text “The film is nice.” of the node “User1” can be expressed by “User1.review=The film is nice.”, where “.review” can be replaced as the property name in the graph.
ster the ability of LLMs on the graph, including graph structure modeling, graph language modeling, graph generation modeling, and graph thought modeling. The first two groups are focused on graph reasoning, the third group is typical graph generation, and the last group aims at both graph reasoning and generation 5. After graph input engineering, we can directly reuse the standard causal language modeling (CLM) objective to continually tune the LLM on such groups. Formally, given one task dataset Dj = {(Ii,Gi,Pi,Ai)}Nji=1, the LLM can be optimized by maximum likelihood with:
L(Dj) = − Nj∑
i=1
log πθ(Yi = Ai|Xi), (1)
where πθ denotes the LLM with trainable parameters θ, Yi is the model output, Xi and Ai respectively represent the input sequence and reference label, which depends on the specific task definition. Table 1 lists all groups of tasks and corresponding clusters to show the task definition, model input,
5We only choose the first three groups of tasks for instruction tuning. The tasks from graph thought modeling are only used for the evaluation.
and output. Therefore, we can obtain an instructionbased graph LLM and named InstructGraph-INS.
2.4 Graph Preference Alignment Recently, the NLP community has witnessed a significant decrease in hallucination through preference optimization (Ouyang et al., 2022; Zhao et al., 2023e; Rafailov et al., 2023; MacGlashan et al., 2017). Following this, we propose graph preference alignment to alleviate the hallucination of LLMs on the graph. As depicted in Figure 2, we intuitively design four typical hallucination circumstances for graph reasoning and generation and perform negative sampling for each graph task.
Hallucinations in Graph Reasoning Typically, the instruction-version LLM may be a strong instruction follower, yet, sometimes fall into hallucinations because of the erroneous input or lack of knowledge: 1) correct graph but wrong answer means the LLM makes a wrong prediction even though the input is legal, 2) unfactual graph but wrong answer means the wrong answer caused by a
graph with unfaithful semantics to external knowledge, 3) conflict graph but wrong answer means there exists conflict information in the input graph, and 4) missing graph but wrong answer means that the input graph is missing some crucial information related to the answer.
To simulate the first circumstance, we can randomly choose a result from other examples to form a negative output Y−i . For the rest, we can randomly replace, add, or remove some nodes (entities) or edges (triples) in the graph and construct a new input with the original instruction and passage. Therefore, the original answer can be viewed as the negative Y−i and the positive Y+i defined as “Sorry, the input graph contains wrong information, so the question is unanswerable directly.”.
Hallucination in Graph Generation Graph generation is harder than reasoning because the LLM needs to output a complete and accurate code-like format sequence. The following are three kinds of wrong-generated graphs: unfactual graph, conflict graph and missing graph. We can directly construct
a wrong graph as the final output Y−i by performing replace, add, and remove operators, which are similar to the graph reasoning. The original graph is denoted as positive Y+i . Additionally, in cases where an incorrect answer is due to a faulty input, we may substitute the original input with an unrelated one from the dataset that doesn’t affect the answer graph. The original answer graph is then considered as the negative output Y−i .
We next use the DPO algorithm to reduce hallucination. Specifically, given one instruction example (Xi,Y+i ) and a corresponding negative (Xi,Y−i ), we can define the preference model under the Bradley-Terry (Bradley and Terry, 1952) as:
pθ(Y+i > Y−i |Xi) = 1
1 + exp{r(Y+i ,Y−i ,Xi)} ,
r(Y+i ,Y−i ,Xi) =− β log πθ(Y+i |Xi) πref (Y+i |Xi)
+ β log πθ(Y−i |Xi) πref (Y−i |Xi) ,
(2)
where β is the balance factor, pθ denotes the pref-
erence model, πθ and πref respectively denotes the policy and reference model, which can be initialized from instruction-version LLM. Thus, we can optimize the LLM by maximum likelihood with:
J (πθ, πref ) = −E(Xi,Y+i ,Y−i )∼D[ log σ ( β log
πθ(Y+i |Xi) πref (Y+i |Xi) − β log πθ(Y − i |Xi) πref (Y−i |Xi) )] . (3)
We denote the policy πθ as InstructGraph-PRE.
3 Experiments
In this section, we perform extensive experiments to evaluate the effectiveness of InstructGraph over graph tasks and general NLP tasks.
3.1 Implementation Settings We construct about 1.6M examples for graph instruction tuning and 100K examples for graph preference alignment. In default, we choose LLaMA27B-HF (Touvron et al., 2023b) from HuggingFace6
6https://huggingface.co/meta-llama.
as the backbone. The maximum length is set as 2048. The optimizer is AdamW. The learning rate is set to 5e − 5 with a decay rate of 0.1 in the graph instruction tuning stage and will be changed to 5e− 7 in the graph preference alignment stage. To accelerate the training7, we utilize FSDP (Zhao et al., 2023d) with CPU Offloading (Tsog et al., 2021), FlashAttention (Dao et al., 2022), and BFloat16 techniques, and utilize LoRA (Hu et al., 2022) to perform parameter-efficient learning with rank = 32 and lora_α = 128.
3.2 Main Results on Graph Instruction Tasks
In this section, we exhaustively evaluate the InstructGraph-INS on multiple graph reasoning and generation tasks in zero-shot settings. We use a code-like format to unify all graphs and construct an instruction tuning test set. Data statistics are shown in Table 8, and the details are shown in Appendix A.1. To make a comparison with a similar scale LLM, we choose the widely-used LLaMA27B and Vicuna-7B as the open-source baseline. In pursuit of investigating the performance level of InstructGraph in the era of AGI, we also choose GPT-3.5 (turbo) (Ouyang et al., 2022) and GPT-
7The implementation is referred to https://github. com/facebookresearch/llama-recipes.
4 (OpenAI, 2023) as strong baselines 8. Table 2 showcases the main results of graph reasoning and generation, we thus draw the following conclusions: 1) InstructGraph-INS achieves the best overall results 79.84% and outperforms GPT4 by 13.08%. 2) Compared with the same scale LLMs, our framework performs the best on all graph tasks, which shows that further instruction tuning over well-designed graph tasks can better improve the reasoning and generation ability. 3) For the tasks Degree Computing, WebNLG, GenWiki, WikiTQ, and Citseer, InstructGraph-INS underperforms GPT-3.5 and GPT-4. Since the LLMs with large-scale parameters have stored more similar knowledge. Despite this, InstructGraph-INS still exhibits approximately 10% better performance on other reasoning tasks.
3.3 Effectiveness of Graph Generation
Additionally, we also expect to delve into whether InstructGraph-INS achieves the improvement on graph generation tasks, We choose two external manners to evaluate the results: 1) NER denotes named entity recognition, and 2) RE denotes relation extraction. As shown in Figure 3, we visualize the comparison performances on three graph gen-
8https://platform.openai.com/.
eration tasks, where Wikidata and UIE belong to knowledge graph construction and NLGraph focus on structure graph generation. We observe that: 1) InstructGraph-INS can bring significant improvement for LLaMA2 and Vicuna, indicating the graph generation ability encompasses NER and RE. 2) We also integrate all baselines with the 2-shot exemplars, the results illustrate that the performance of InstructGraph-INS is consistently the highest. 3) RE is more challenging to NER because it involves understanding the semantics of generated nodes (entities) and making decisions on their relation or weight. Despite this, the improvement of RE is larger than NER, which signifies that graphspecific optimization can better empower the LLM in constructing triples.
3.4 Main Results on Graph Preference Tasks
We next explore whether InstructGraph can reduce the graph hallucination problem. We sample a few tasks from the corresponding cluster to build a hallucination testing set, including structure, caption, graph question answering, and node classification. The data statistics are shown in Table 8, and the details are shown in Appendix A.2. Specifically, each example consists of a correct answer and a wrong answer, we calculate the LLM’s perplexity (PPL) on these answers and choose the option with the lowest PPL score as the preference results. Therefore, the accuracy metric can reflect the performance of hallucination mitigation.
As shown in Table 3, we choose LLaMA2, Vicuna, and two variants of InstructGraph to make a comparison. InstructGraph-INS outperforms LLaMA2 and Vicuna by 16.44% and 15.46%, respectively, demonstrating that our framework with only graph instruction tuning can solve the preference tasks better. This indicates that injecting task-related knowledge into the LLM’s intrinsic parameter can be one of the significant factors for hallucination reduction. Furthermore, InstructGraphPRE significantly enhances the instruction version model by about 10%, demonstrating that welldesigned preference optimization can hit the upper boundary and endow the LLM with the ability to alleviate the pitfalls of hallucination.
3.5 Effectiveness of Thought Planning
Recall the graph instruction tuning, we are eager for the LLM to solve the thought planning tasks,
including arithmetic, symbolic, robotic, and logic. We design two few-shot scenarios: 1) Chain-ofThought (CoT) directly sampling few-shot exemplars with manually annotated sequence rationales to form a prompt. 2) Graph Thought Modeling (GTM) decomposes the sequence rationale into three stages, i.e., finding topic entities or keywords, building a graph to express the thought, and outputting the final answer. The comparison results are depicted in Table 4, and we can observe that InstructGraph-INS achieves the best performance when elicited by CoT and GTM prompts. In addition, GTM sometimes performs below expectations in the tasks of SVAMP, Letter, and ProofWriter. We believe that these tasks are difficult to express using an explicit graph to convey the thinking process.
3.6 Ablation Study
In this section, we focus on the ablation study to show how much each component contributes to performance. We choose three clusters for the test, i.e., Graph QA, Node CLS, and IE. As shown in Table 5, the results illustrate that the performance drops when removing one of these components. For the instruction tuning testing, we can observe that graph language modeling plays a significant role in Graph QA and Node CLS clusters, while graph generation modeling is beneficial to the performance of IE. For the preference testing, we can see that the performance of w/o. missing graph drops significantly, indicating that the major factor of hallucination is the lack of key information in the input graph or generated graph.
4 Further Analysis
4.1 Effectiveness on General Preference Tasks
We also delve into whether the preference optimization on the graph data hinders the effectiveness in the general domains. To reach this goal, we choose three external preference and hallucination tasks. 1)
HaluEval (Li et al., 2023a) 9 focuses on hallucination evaluation in dialogue, general understanding, question answering, and text summarization (abstract). 2) TruthfulQA (Lin et al., 2022) 10 aims to test the factuality of LLMs on knowledge-intensive tasks. We choose MC1 as the test. 3) AnthropicHH (Bai et al., 2022) 11 has released the evaluation set for both harmless and helpful perspective. For these tasks, we do not perform task-specific fine-tuning to show the zero-shot performance. Results in Table 6 showcase that our framework occasionally outperforms the sample scale baselines on some tasks, which meets our desiderata.
4.2 Performance on General NLP Tasks
We next evaluate the performance of InstructGraph on the general NLP tasks. We choose Big-Bench-Hard (BBH) (Suzgun et al., 2023) and Massive Multitask Language Understanding (MMLU) (Hendrycks et al., 2021) benchmarks with few-shot exemplars to perform reasoning. As shown in Table 7, even though these tasks do not belong to graph domains, we can still obtain competitive results compared with other same-scale open-source LLMs.
9https://github.com/RUCAIBox/HaluEval. 10https://github.com/sylinrl/TruthfulQA. 11https://github.com/anthropics/hh-rlhf.
5 Related Work
5.1 LLMs for Graph Learning
A series of works have studied how to leverage LLMs to solve graph-centric tasks (Jin et al., 2023), which can be decomposed into the following categories: 1) Prompt engineering. A series of works aims to design the interface to elicit the LLM to better understand and reason on the graph (Ye et al., 2023; Han et al., 2023; Zhang et al., 2023b; Zhang, 2023; Kim et al., 2023; Wang et al., 2023b; Luo et al., 2023; Wang et al., 2023a; Guo et al., 2023; Zhao et al., 2023b). 2) Boosting LLMs with trainable GNNs. This kind of method focuses on enhancing the LLMs with trainable GNNs which can capture the arbitrary scale of the graph (Zhang et al., 2022; Chai et al., 2023; Tang et al., 2023; Zhao et al., 2023a; Tian et al., 2023; Qin et al., 2023). 3) Instruction tuning over graph data. Similar to ours, Xu et al. (2023); Jiang et al. (2023); Fang et al. (2023); Zeng et al. (2023) directly collect some graph or symbol data to form an instruction corpus, and then continually pre-train the LLM. Different from them, our InstructGraph further empowers the LLM by graph instruction tuning with the code-like universal format and well-designed hallucination alleviation strategy by preference alignment.
5.2 Hallucination in LLMs
LLMs usually generate seemingly plausible answers, which is called hallucination (Ji et al., 2023; Zhang et al., 2023a). The phenomenon of hallucination encompasses fabricating erroneous user input, unfaithful for previously generated context, and unfactual for external knowledge and commonsense. To estimate hallucination, Kryscinski et al. (2020); Li et al. (2023a); Tam et al. (2023); Min et al. (2023) leverage external tools or neural networks (e.g., BERT-NLI, GPT-4) to score the faithfulness and factuality of the model output. Recently, many works focus on suppressing this problem by retrieval-augmented generation (RAG) (Lewis et al., 2020), contrastive learning (Sun et al., 2023), contradictory evaluation (Mündler et al., 2023), and decoding strategies (Lee et al., 2022; Shi et al., 2023; Li et al., 2023b). Different from them, we aim to solve the hallucination problem on graph tasks with preference alignment.
5.3 Parameter-Efficient Learning Study
To accelerate the training speed and reduce memory usage under the limitation of sources, we lever-
age parameter-efficient learning (PEL) techniques to equip the original LLM with only a few trainable parameters. To study the choice of different PEL methods, we compare LoRA with other PEL methods, such as Prefix-tuning (Li and Liang, 2021) 12, and Adapter (Houlsby et al., 2019). For each method, we choose six different scales and perform graph instruction tuning over 10% training data. The balance between trainable parameters and averaged results is visualized in Figure 4. We can see that LoRA can achieve the best performance and is similar to full fine-tuning regardless of the scale of trainable parameters.
6 Conclusion
This paper proposes a novel InstructGraph framework that empowers the LLM with the capacity to solve graph reasoning and generation tasks. To bridge the gap between graph data and textual language models, we introduce a structured format verbalizer to transform each graph into a code-like format and continually tune the LLM based on the instruction dataset, which is collected from 29 graph tasks. In addition, we also introduce a graph preference alignment stage to further mitigate the hallucination problem when reasoning on or generating a graph. Extensive experiments illustrate that InstructGraph substantially achieves the best performance. In our future work, we will further improve the performance on graph-centric and general NLP tasks and scale it to other LLMs.
12Prefix-Embedd: only tune the input embeddings layer; Prefix-Layer: tune each transformer layer.
Limitations
Our work is based on continual optimization for large language models and achieves outstanding performance across several benchmarks. However, it still carries the following limitations: (1) Due to resource limitations, we only conduct full experiments and analysis on a 7B scale. For 13B scales, we use 10% of the original training set for the model training. We plan to perform full parameter optimization on other backbones beyond 13B in the future. (2) The proposed structured format verbalizer aims to create a code sequence that describes a graph, but the input length may be limited when dealing with complex graphs or in a few-shot in-context learning setting.
Social Impact and Ethics
In terms of social impact, the graph data we utilize are all from publicly available data sources. Infusing this graph information into the model’s reasoning process will not introduce additional bias. However, the open-source backbones we used may have some negative impacts, such as gender and social bias. Our work would unavoidably suffer from these issues. We suggest that users should carefully address potential risks when the proposed method is deployed online.
Acknowledgement
This work is supported by National Natural Science Foundation of China under Grant No. 62202172, No. 62377012, No. U1911203, and No. 42375146, and Shanghai Science and Technology Committee General Program No. 22ZR1419900.
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A Details of the InstructGraph Corpus
In this section, we provide some details of the corpus construction including both instruction and preference perspective.
A.1 Instruction Tuning Dataset
To merge all graph-oriented reasoning and generation tasks, we collect and construct 29 tasks to form instruction data. We do not construct training sets for graph thought modeling.
Graph Structure Modeling Graph structure modeling aims to urge the LLM to understand the structure of a graph along with the corresponding task-specific instruction. To reach this aim, we collect structure dataset NLGraph (Wang et al., 2023a). The original dataset consists of 8 different tasks, such as Connectivity Detection, Cycle Detection, Topological Sorting, Shortest Path Computing, Maximum Flow Computing, Bipartite Graph Matching, Hamilton Path Detection and GNN Embedding. Yet, the authors Wang et al. (2023a) mentioned that the current LLMs are hard to perform on more complex graph reasoning, such as Topological Sorting, Maximum Flow Computing, and
GNN Embedding, so we remove them. In addition, we also random sample some graphs of NLGraph, and construct a Degree Computing task.
• Connectivity Detection: detect whether there exists a path between two nodes in the graph. This task is a binary classification and the answer should be ’The answer is yes’ or ’The answer is no’.
• Cycle Detection: determine if there is a cycle in this graph. This task is a binary classification and the answer should be ’Yes’ or ’No’.
• Topological Sorting: determine if there is a path that visits every node exactly once in this graph. This task is a binary classification and the answer should be ’Yes’ or ’No’.
• Bipartite Graph Matching: detect whether there exists an edge between two given nodes
in a bipartite graph. This task is a binary classification and the answer should be ’Yes’ or ’No’.
• Shortest Path Computing: find the shortest path between two nodes in the graph, and calculate the sum of the weights in the shortest path. The answer is a sequence of the path with a value.
• Graph Degree Computing: calculate the degree of the target node in the graph. The answer is an integer value.
Graph Language Modeling Graph language modeling aims to teach the LLM to understand both the structure and semantics knowledge of the graph and answer the question. We decompose this group into 6 kinds of tasks, including graph caption generation, graph question answering, graph node classification, graph link prediction, graph relevance inspection, and graph collaboration filtering.
• Graph caption generation: generate an encyclopedia passage when given a knowledge graph with all entities and structure triples
representing factual and commonsense knowledge. We directly choose the datasets from WebNLG (Gardent et al., 2017), GenWiki (Jin et al., 2020), EventNarrative (Colas et al., 2021), XAlign (Abhishek et al., 2022). In addition, we also follow (Wang et al., 2022) to collect the Wikipedia corpus and corresponding wikidata knowledge graph to build the caption task. Specifically, we use the AC automatic machine algorithm to recognize all entities in the passage and construct a 2-hop sub-graph based on the topic entity.
• Graph question answering: find an entity and a reasoning path in the graph to answer the question. We directly collect the corpus from PathQuestions (Zhou et al., 2018), GrailQA (Gu et al., 2021), WebQuestions (Berant et al., 2013), WikiTableQuestions (Pasupat and Liang, 2015). Especially, the WikiTableQuestions is a table understanding task that answers a question based on the table. To make our framework support this kind of task, we perform preprocessing that transforms each row line of the table into a single graph, where the table head is the relation name and each cell is the entity.
• Graph node classification: classify the target node based on the corresponding graph. We directly choose from Cora (McCallum et al., 2000), Citeseer (Giles et al., 1998), Pubmed (Sen et al., 2008), OGBN-ArXiv, and OGBN-Products (Hu et al., 2020). Because the graph in these tasks is too big, we only sample a 2-hop sub-graph of centering each target node. We also perform down-sampling for each task.
• Graph link prediction: classify the edge (relation) between two given nodes (entities) based on the graph. We choose three main knowledge graph, such as Wikidata (Wang et al., 2021), Freebase (Bollacker et al., 2008), ConceptNet (Speer et al., 2017). Specifically, we random sample a subset of triples, and then extract and merge two 2-hop sub-graphs that center with two entities, respectively.
• Graph relevance inspection: inspect whether the caption is relevant to the graph. The task is a binary classification with two categories, i.e., "relevant" and "irrelevant". We directly use the same corpus from wikipedia (Wang et al., 2022) in graph caption generation task. For the negative sampling of each graph, we directly choose other captions.
• Graph Collaboration Filtering: predict the score that the user node prefers to the target item node based on the collaboration graph. We choose the widely used Amazon (He and McAuley, 2016) as the corpus. Because the Amazon dataset does not provide any graph data, we thus perform a preprocessing stage to construct a collaboration graph. Specifically, we calculate the Jaccard similarity between each pair of users based on their preference items and then recall the top-10 similarity users for each user to form a graph. Hence, we can inject this graph into the LLM to let it know how to recommend some items based on all potential users.
Graph Generation Modeling This group aims to guide the LLM to generate a graph in a codelike format. We consider two challenging graph generation domains, including, knowledge graph generation and structure graph generation.
• Knowledge graph generation: similar to information extraction which aims to extract
entities and relations when given one passage. We directly choose the corpus from unified information extraction (UIE) (Wang et al., 2023c; Gui et al., 2023), which consists of 21 used named entity recognition (NER) tasks, 10 used relation extraction (RE), and 4 used event extraction (EE).
• Structure graph generation: generate a structure graph based on the description. For example, when given a graph description is “Please generate a full-connection un-directed graph with four nodes ranging from 0 to 3.”, the expected codelike format graph is “Graph[name=’structuregraph’]node_list=[0, 1, 2, 3]; edge_list=[(0 <-> 1), (0 <-> 2), (0 <-> 3), (1 <-> 2), (1 <-> 3), (2 <-> 3)];”. We can directly reuse the corpus from NLGraph (Wang et al., 2023a) and sample a subset to build this task.
A.2 Preference Alignment Dataset
We have selected a partial dataset from the graph instruction tuning dataset for preference alignment. This dataset includes Connection Detection, Cycle Detection, Shortest Path Computing, Degree Computing, Graph Caption with Wikipedia and WebNLG, Graph QA with PathQSP, Node CLS with Cora, Citeseer, Pubmed, Arxiv, and Products, and IE with Wikipedia.
For each task, we design positive and negative answers to support preference alignment. Details are shown in Table 9.
B Further Analysis
B.1 Effectiveness of Code Format Graph
In this part, we evaluate the use of the structured format verbalizer when aligning the graph structure to the textual LLM. We choose four classic graph reasoning and generation tasks, i.e., PathQSP,
WebNLG, CoRA, and UIE. To compare with the structured format verbalizer, we directly choose the heuristic template introduced by InstructGLM (Ye et al., 2023) to describe each path in the graph. For example, the path “(e1, r1, e2), (e2, r2, e3)” can be formulated as “e1 is connected with e3 within tow hops through e2, and featured relations r1 and r2”. We use this template to prompt GPT-4 and LLaMA2 to show the performance. The results in Table 10 demonstrate that our structured format verbalizer outperforms traditional templates in all tasks. Especially, the LLM with traditional templates cannot support graph generation, while the structured format verbalizer can reach this goal. B.2 Effectiveness of Different Backbones To investigate whether the proposed InstructGraph can consistently improve the graph reasoning and generation ability with different LLMs, we select LLaMA2-7B, LLaMA2-13B, Vicuna-7B, and Vicuna-13B as the start checkpoints. To make the experiment efficient, we randomly choose 10% training data to perform graph instruction tuning and make a comparison with the corresponding vanilla LLMs. Results in Figure 5 show that InstructGraph can consistently achieve substantial improvement for arbitrary backbones and scales. Additionally, we observe that Vicuna has better performance than LLaMA2 initially. However, after graph instruction tuning, this trend is re-
versed. Upon further analysis, we find that both LLaMA2 and Vicuna were re-optimized based on LLaMA (Touvron et al., 2023a). Vicuna’s optimization involves using supervised fine-tuning (SFT) to inject domain knowledge with massive conversation data into LLaMA. Meanwhile, LLaMA2 focuses on refactoring the model architecture and pre-training strategy to improve the model’s versatility. Thus, Vicuna may have a better ability to understand instructions than LLaMA2. Despite this, LLaMA2 can be the better starting checkpoint for boosting LLMs on graph reasoning and generation tasks with parameter updates.
B.3 Human Evaluation We end this section with a case study to demonstrate the performance of LLMs when solving graph reasoning and generation tasks. We choose LLaMA2 (7B) to make a comparison and respectively choose one example from graph caption generation and knowledge graph generation. For the answer, we perform a human evaluation to estimate the effectiveness of InstructGraph. As shown in Table 11, InstructGraph can outperform all the baselines. Specifically, compared with LLaMA2, InstructGraph can generate more natural and readable captions to describe factual information. For the graph generation, InstructGraph can provide accurate entities and triples.

heading: Future Work
text: In our future work, we will further improve the performance on graph-centric and general NLP tasks and scale it to other LLMs. 12Prefix-Embedd: only tune the input embeddings layer; Prefix-Layer: tune each transformer layer. Limitations
Our work is based on continual optimization for large language models and achieves outstanding performance across several benchmarks. However, it still carries the following limitations: (1) Due to resource limitations, we only conduct full experiments and analysis on a 7B scale. For 13B scales, we use 10% of the original training set for the model training. We plan to perform full parameter optimization on other backbones beyond 13B in the future. (2) The proposed structured format verbalizer aims to create a code sequence that describes a graph, but the input length may be limited when dealing with complex graphs or in a few-shot in-context learning setting