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## RAG vs. GraphRAG: A Systematic Evaluation and Key Insights
### Haoyu Han[1], Yu Wang[2], Harry Shomer[1], Yongjia Lei, [2], Kai Guo [1], Zhigang Hua[3], Bo Long [3], Hui Liu[1], Jiliang Tang[1]
1Michigan State University, 2University of Oregon, 3Meta
### {hanhaoy1, shomerha, guokai1, liuhui7, tangjili}@msu.edu {yu... | {
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tracts a Knowledge Graph (KG) from text and performs retrieval solely based on the KG and (2)
Community-based GraphRAG (Edge et al., 2024),
which retrieves information not only from the constructed KG but also from hierarchical communities within the graph. For the Question Answering task, we conduct experiments on bot... | {
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Figure 1: The illustration of RAG, KG-based GraphRAGs and Community-based GraphRAGs.
Despite leveraging the existing graphs, recent
studies have explored incorporating graph construction into GraphRAG to enhance text-based
tasks. For example, Dong et al. (2024) construct
document graphs using Abstract Meaning Represe... | {
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content. Additionally, for each triplet, we can
retrieve the corresponding text associated with
it. We define two variants of KG-GraphRAG: (1)
_KG-GraphRAG (Triplets), which retrieves only the_
triplets, and (2) KG-GraphRAG (Triplets+Text),
which retrieves both the triplets and their associated
source text. We implemen... | {
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Table 1: Performance comparison (%) on NQ and Hotpot datasets. The best results are highlighted in bold, and the
second-best results are underlined.
**NQ** **Hotpot**
**Method** **Llama 3.1-8B** **Llama 3.1-70B** **Llama 3.1-8B** **Llama 3.1-70B**
P R F1 P R F1 P R F1 P R F1
RAG **71.7** **63.93** **64.78** **74.55... | {
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40.0 17.1
13.7 29.1
45.4 9.2
9.8 35.6
(a) NQ
47.2 7.8
6.0 39.0
(b) Hotpot
55.4 11.6
13.6 19.4
(c) MultiHop-RAG
(d)... | {
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(a) Llama3.1-8B (b) Llama3.1-70B
Figure 3: Overall QA performance comparison of different methods.
We evaluate both the KG-based and Communitybased GraphRAG methods, along with the Integration strategy discussed in Section 4.4. The results of Llama3.1-8B model on Query-based single
document summarization and multipl... | {
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Table 4: The performance of query-based single document summarization task using Llama3.1-8B.
**SQuALITY** **QMSum**
**Method** **ROUGE-2** **BERTScore** **ROUGE-2** **BERTScore**
P R F1 P R F1 P R F1 P R F1
RAG 15.09 8.74 10.08 74.54 81.00 77.62 21.50 **3.80** 6.32 **81.03** 84.45 **82.69**
KG-GraphRAG (Triplets o... | {
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### Limitations
In this paper, we evaluate and compare RAG and
GraphRAG on Question Answering and Querybased Summarization tasks. Future work can extend this study to additional tasks to further assess
the strengths and applicability of GraphRAG. Additionally, the graph construction in all GraphRAG
methods explored in... | {
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retrieval on structured data. _arXiv preprint_
_arXiv:2305.19912._
Yongqi Li, Wenjie Li, and Liqiang Nie. 2022. Dynamic
graph reasoning for conversational open-domain
question answering. ACM Transactions on Infor_mation Systems (TOIS), 40(4):1–24._
Chin-Yew Lin. 2004. Rouge: A package for automatic
evaluation of summ... | {
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Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, and Zhen-Hua Ling.
2024. Corrective retrieval augmented generation.
_arXiv preprint arXiv:2401.15884._
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and
Christopher D Manning. 2018. Hotpotqa: A dataset
for diverse, explainable multi-hop qu... | {
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### A Appendix
**A.1** **Dataset**
In this section, we introduce the used datasets in the question answering tasks and query-based summarization tasks.
**A.1.1** **Question Answering**
In the QA tasks, we use the following four widely used datasets:
- Natural Questions (NQ) (Kwiatkowski et al., 2019): The NQ dat... | {
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- ODSum (Zhou et al., 2023): The ODSum dataset is designed to evaluate modern summarization
models in multi-document contexts and consists of two subsets: ODSum-story and ODSum-meeting.
ODSum-story is derived from the SQuALITY dataset, while ODSum-meeting is constructed from
QMSum. We use both ODSum-story and ODSum-m... | {
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Table 10: The performance of Community-GraphRAG (Local) with Llama 3.1-70B model on NovelQA dataset.
Community-GraphRAG (Local) character meaning plot relat settg span times avg
mh 77.08 70.59 63.89 77.53 92.31 28 32.35 46.68
sh 68.42 71.43 74.9 62.5 74.23 - - 72.44
dtl 55.71 37.08 69.64 64.29 75.68 ... | {
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Table 13: The performance of query-based multiple document summarization task using Llama3.1-70B.
ODSum-story ODSum-meeting
Method ROUGE-2 BERTScore ROUGE-2 BERTScore
P R F1 P R F1 P R F1 P R
RAG 15.60 9.98 11.09 74.80 81.29 77.89 18.81 6.41 8.97 83.56 85.16
KG-GraphRAG(Triplets only) 10.08 9.12 8.48 75.71 81.93 78... | {
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|RAG-Order 1 GraphRAG-Local-Order 1 RAG-Order 2 GraphRAG-Local-Order 2|Col2|Col3|Col4|RAG-Order 1 GraphRAG-Local-Order 1 RAG-Order 2 GraphRAG-Local-Order 2|Col6|Col7|Col8|Col9|Col10|
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... | {
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## Retrieval-Augmented Generation with Graphs (GraphRAG)
**Haoyu Han[1][∗], Yu Wang[2][∗], Harry Shomer[1], Kai Guo[1], Jiayuan Ding[5], Yongjia Lei[2],**
**Mahantesh Halappanavar[3], Ryan A. Rossi[4], Subhabrata Mukherjee[5], Xianfeng Tang[6], Qi He[6],**
**Zhigang Hua[7], Bo Long[7], Tong Zhao[8], Neil Shah[8], Ami... | {
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consultation [467, 472, 515]). Moreover, recent advancements in large language models (LLMs)
have further underscored the power of RAG in enhancing the social responsibility of LLMs, such as
mitigating hallucinations [397], enhancing interpretability and transparency [203], enabling dynamic
adaptability [360, 419], red... | {
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star Methane, and 3-star T-junction vs. 4-square road, is essential in drug design for disease
treatment [139] and road construction for city planning [209]. Beyond the retriever, the generator
also requires specialized designs. When retrieved content includes complex graph structures
with textual attributes, simply ve... | {
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Figure 1: RAG works on text and images, which can be uniformly formatted as 1D sequences or 2D
grids with no relational information. In contrast, GraphRAG works on graph-structured data, which
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Figure 3: A holistic framework of GraphRAG and representative techniques for its key components.
### 2 A Holistic Framework of GraphRAG
Based on existing literature on GraphRAG, we present a holistic framework of GraphRAG. Next, we
introduce the basic problem setting and... | {
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Table 1: Summary of Task Applications and Exemplary Queries for GraphRAG in each domain.
-----
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Figure 4: Existing techniques of query processor Ω[Processor] in GraphRAG.
Table 2: Difference of query processor Ω[Processor] between RAG and GraphRAG.
**Technique** **RAG** **GraphRAG**
Entity Recognition Extracting mentions in knowledge bases Extracting mentioned node... | {
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match the recognized types for next-round exploration. For example, given the question "Who are the
authors of ‘Language Models are Unsupervised Multi-task Learners’?" the initially recognized entity
should not only be based on the semantic name "Language Models are Unsupervised Multi-task
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"p... |
Figure 5: Visualizing representative retrievers used in GraphRAG.
Table 3: Categorizing representative retrievers used in GraphRAG.
**Method/Strategy** **Input** **Output** **Description**
Entity Linking Entity Mention Node Match query entity and graph node
Relational Ma... | {
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accurate entity recognition conducted by the query processor and the quality of labeled entities on
graph nodes. This technique is commonly applied in knowledge graphs, where Top-K nodes are
selected as starting points based on their textual similarity to the query. The similarity metric can
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**2.4.2** **Learning-based Retriever**
One significant limitation of heuristic-based retrievers is their over-reliance on pre-defined rules,
which limits their generalizability to data that does not strictly adhere to these rules. For example,
when confronted with entities that have slight semantic or structural varia... | {
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**x[l]i** [=][ γ][Θ]γ
**e[l]ij** [=][ γ][Θ]γ
x[l]i[−][1] _⊕_ � _ϕΘϕ_ �x[l]i[−][1], xj[l][−][1], eij� _,_ Node-level (3)
_j∈Ni_
**e[l]ij** [=][ γ][Θ]γ e[l]ij[−][1] _⊕_ � _ϕΘϕ_ �e[l]ij[−][1][,][ e]mn[l][−][1][,][ x][e]ij _[∩][e]mn�_ _,_ Edge-level (4)
_emn∈Nij[e]_
**G[l]** = ρΘρG ({x[l]i[,][ e][l]ij... | {
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**Integrated Retrieval: Integrated retrieval combines various types of retrievers to capture relevant**
information by balancing their strengths and weaknesses. Typically, integrated retrieval approaches
are categorized according to which individual retrievers are used in combination, with notable
examples including ne... | {
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field enlarges (i.e., the number of hops increases) in the retrieved subgraphs would also exponentially
increase the amount of context length in the prompt and dilute the focus of LLMs on the taskrelevant knowledge [358]. This poses a new requirement for the graph-based reranking mechanism to
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**2.5.3** **Graph Augmentation**
Graph augmentation aims to enrich the retrieved graph to either enhance the content or improve
the robustness of the generator. This process can involve adding supplementary information to the
retrieved graph, sourced from external data or knowledge embedded within LLMs. There are two
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decomposes the question, generates a reasoning trace, and identifies the specific knowledge needed
for the current step; and Summarization, where it summarizes the relevant knowledge from the
subgraph that retrieved based on the current reasoning trace.
**2.6** **Generator**
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**2.6.3** **Graph-based Generator**
In the scientific graph domain, GraphRAG generators often go beyond LLM-based methods due to
the need for accurate structure generation. RetMol [433] is particularly versatile because it can work
with various encoder and decoder architectures, supporting multiple generative models a... | {
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Note that the above-mentioned data structures can only represent basic graphs without support
for complex scenarios such as multi-relational edges or edge attributes. For instance, using an
adjacency matrix to represent a multi-relational attributed graph requires an expanded structure:
**A ∈** R[|V|×|V|×|R|], where R ... | {
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- Rule-based construction: Many traditional approaches use rule-based techniques for constructing
the graph. This takes the form of custom parsers and manually defined rules used to extract facts
from raw text. Note that these parsers can differ depending on the source of the text. Prominent
examples include ConceptNet... | {
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the entity is to the query. For Yasunaga et al. [493], if |V | > 200, they randomly sample 200
entities. Sun et al. [381] use a version of beam-search to explore the KG. Jiang et al. [185], Feng
et al. [110] extract all paths of length k ≤ 2 between seed entities. Alternatively, LARK [62]
retrieves all facts that lie o... | {
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representation based on the LLM-encoded query. They then run L rounds of message passing
where after each layer l only the top-K new edges are kept, resulting in a set of entities Cq[l] [. This]
is determined by a learnable attention weight, which prunes the other edges from the graph. The
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**Other retrievers: KICGPT [439] is concerned with the task of knowledge graph completion, where**
given a partial fact (h, r, ∗), we want to predict the correct entity ˆe. KICGPT retrieves the entities
by first scoring all possible entities using a traditional KG embedding score function. That is, for a
score function... | {
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LLM is given the relational and textual information of each, and is asked to give it a score from 0
to 1, which is then used for re-ranking. GenTKG [246] orders the paths by the time they occurred,
further including the time with each. KICGPT [439] ranks all entities using the score of the KG
embedding score function, ... | {
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2... |
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