# DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer’s Disease
Questions with Scientific Literature

Dawei Li1, Shu Yang2∗, Zhen Tan1, Jae Young Baik2, Sukwon Yun3, Joseph Lee2,
Aaron Chacko2,
Bojian Hou2, Duy Duong-Tran2,4, Ying Ding5, Huan Liu1, Li Shen2†, Tianlong
Chen3†
1School of Computing, and Augmented Intelligence, Arizona State University
2Department of Biostatistics, Epidemiology and Informatics, University of
Pennsylvania Perelman School of Medicine
3Department of Computer Science, The University of North Carolina at Chapel
Hill
4Department of Mathematics, United States Naval Academy
5School of Information, The University of Texas at Austin, Austin
Equal Constributions Corresponding authors

###### Abstract

Recent advancements in large language models (LLMs) have achieved promising
performances across various applications. Nonetheless, the ongoing challenge
of integrating long-tail knowledge continues to impede the seamless adoption
of LLMs in specialized domains. In this work, we introduce DALK, a.k.a.
Dynamic Co-Augmentation of LLMs and KG, to address this limitation and
demonstrate its ability on studying Alzheimer’s Disease (AD), a specialized
sub-field in biomedicine and a global health priority. With a synergized
framework of LLM and KG mutually enhancing each other, we first leverage LLM
to construct an evolving AD-specific knowledge graph (KG) sourced from AD-
related scientific literature, and then we utilize a coarse-to-fine sampling
method with a novel self-aware knowledge retrieval approach to select
appropriate knowledge from the KG to augment LLM inference capabilities. The
experimental results, conducted on our constructed AD question answering
(ADQA) benchmark, underscore the efficacy of DALK. Additionally, we perform a
series of detailed analyses that can offer valuable insights and guidelines
for the emerging topic of mutually enhancing KG and LLM. We will release the
code and data at https://github.com/David-Li0406/DALK.

DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer’s Disease
Questions with Scientific Literature

Dawei Li1††thanks:  Equal Constributions, Shu Yang2∗, Zhen Tan1, Jae Young
Baik2, Sukwon Yun3, Joseph Lee2, Aaron Chacko2, Bojian Hou2, Duy Duong-
Tran2,4, Ying Ding5, Huan Liu1††thanks:  Corresponding authors, Li Shen2†,
Tianlong Chen3† 1School of Computing, and Augmented Intelligence, Arizona
State University 2Department of Biostatistics, Epidemiology and Informatics,
University of Pennsylvania Perelman School of Medicine 3Department of Computer
Science, The University of North Carolina at Chapel Hill 4Department of
Mathematics, United States Naval Academy 5School of Information, The
University of Texas at Austin, Austin

##  1 Introduction

Alzheimer’s Disease (AD) is a neurodegenerative disorder characterized by
progressive declines in cognitive and functional status over a span of decades
Report (2023). However, current AD therapy developments are facing critical
challenges due to the lack of knowledge and understanding of the underlying
etiological mechanisms of the disease. Although scientific literature and
dedicated biomedical databases could supply rich sources of AD knowledge,
manual review of relevant information is impossible due to the large volume.

As large language models (LLMs) (Brown et al., 2020; Zhang et al., 2022; Anil
et al., 2023; Touvron et al., 2023) with chain-of-thought (CoT)-based
prompting Wei et al. (2022); Wang et al. (2022); Tong et al. (2023); Yao et
al. (2023); Besta et al. (2023) demonstrate strong language capabilities
across various tasks, there have been attempts to leverage LLMs-based systems
in general biomedical and AD-related applications Mao et al. (2023); Li et al.
(2023c); Yan et al. (2024); Feng et al. (2023). However, while the LLMs have
shown promising performances in many general tasks, recent studies revealed
LLMs’ limitations in long-tail Kandpal et al. (2023) and domain-specific Li et
al. (2023b, 2024) knowledge, thereby significantly impeding their adaptations
in vertical fields such as AD. To deal with this issue, the most common
strategies are retrieval augmented generation (RAG) and domain-specific LLMs
training.

Nevertheless, directly applying these strategies in the context like AD would
still suffer from several issues. First, Data Quality: As in many biomedical
fields, scientific literature composes the largest publicly available corpus
source in AD. Yet, the dense and information-overloaded nature of scientific
literature, when combined with automatic retrieval methods, can lead to the
retrieval of irrelevant and noisy information. Previous research has shown
that noisy and irrelevant corpora can significantly undermine the performance
of LLMs Yu et al. (2023); Chen et al. (2024); Wu et al. (2024). Second,
Efficiency & Scale Issues: Being an critical field of research, the knowledge
of AD is rapidly evolving with scientific advancements at a remarkable pace
and scale. However, retraining a domain-specific LLM or updating certain
knowledge in it demands substantial computational resources Hu et al. (2021);
Ovadia et al. (2023); Zhang et al. (2024). This efficiency issue would also
limit the sizes of domain-specific LLMs, consequently affecting their
performances.

To tackle these limitations, here we propose a Dynamic Co-Augmentation of LLMs
and KG (DALK) framework that facilitates mutual benefits between LLMs and
knowledge graphs (KG) for the AD domain. Initially, our framework addresses
the data quality challenge by extracting more structural and accurate
knowledge from unstructured and dense scientific literature and constructing a
domain-specific knowledge graph tailored to AD. We employ two widely utilized
knowledge graph construction methods, namely pair-wise construction Carta et
al. (2023); Wadhwa et al. (2023) and generative construction Han et al.
(2023); Bi et al. (2024), to comprehensively assess their impact on knowledge
graph quality. Then, we adopt a coarse-to-fine sampling method with a novel
self-aware knowledge retrieval approach to select appropriate knowledge from
the knowledge graph and thus further address the data quality problem.
Notably, the tuning-free nature of our framework significantly enhances
efficiency and facilitates its application in large-scale and API-based
language models OpenAI (2022). In the evaluation section, we derive an
Alzheimer’s Disease question answering (ADQA) benchmark from existing general
medical QA datasets with millions of samples filtered by a curated keyword
list and self-sampling of LLMs. Our extensive experiment on ADQA demonstrates
the effectiveness of our framework in domain-specific applications compared
with general biomedical LLMs and retrieval augmented models. Further
evaluation and analysis provide valuable insights into constructing high-
quality knowledge graphs and sampling accurate knowledge from them.

In summary, our contribution in this work can be summarized as follows:

  * •

We identify the constraints of the current methods for LLMs in domain-specific
areas like AD and introduce DALK, a co-augmentation framework of the LLM and
KG to address these issues.

  * •

We build AD-specific KG and QA benchmark. Through extensive comparisons with
other methods, we showcase the effectiveness of DALK.

  * •

We delve into a comprehensive analysis of our proposed method and provide
valuable insights and guidance on how to construct a high-quality KG and
sample accurate knowledge from it.

##  2 Related Work

##### The interplay between LLMs and KGs

KGs Miller (1995); Speer et al. (2017); Vrandečić and Krötzsch (2014) serve as
structured representations of factual knowledge, typically expressed as (head,
relation, tail) triples. Their structured, factual, and interpretable nature
renders them excellent complements to parametric language models Pan et al.
(2024). Recently, with the rise of large language models (LLMs), numerous
studies have delved into exploring the synergy between LLMs and KGs for
various purposes Pan et al. (2024); Tan et al. (2024). There are a lot of
efforts in conducting knowledge graph construction Carta et al. (2023); Wadhwa
et al. (2023); Han et al. (2023); Bi et al. (2024); Datta et al. (2024),
completion Wei et al. (2023); Zhang et al. (2023b); Li et al. (2024) with the
aid of LLMs. Conversely, other works aim to enhance LLMs by integrating
knowledge sampled from KGs during both training Tang et al. (2023); Luo et al.
(2024); Dernbach et al. (2024); Rangel et al. (2024) and inference Kim et al.
(2023); Wen et al. (2023); Jiang et al. (2023); Sun et al. (2023a) times. Our
work distinguishes itself by proposing a co-augmentation framework for LLMs
and KGs, facilitating their mutual enhancement, and applying it to the domain
of AD.

##### LLMs and KGs for AD research

LLMs and KGs have both been applied to Alzheimer’s Disease research in
previous studies. Pre-trained language models are utilized to work on AD
detection and many other related tasks based on speech recordings and
transcripts Balagopalan et al. (2020); Agbavor and Liang (2022), electronic
health records (EHRs) Mao et al. (2023); Li et al. (2023c); Yan et al. (2024),
and tabular data Feng et al. (2023). KGs have been widely used in biomedical
research, yet only a few are specifically for AD research Romano et al.
(2023); Pu et al. (2023); Hsieh et al. (2023); Nian et al. (2022);
Daluwatumulle et al. (2023). These KGs were generally constructed from a
variety of information derived from heterogeneous biomedical databases (e.g.
for genes, drugs, pathways, etc.) or scientific literature related to AD.
Despite the aforementioned efforts for LLMs and KGs in AD research, no prior
study has explored using LLM to augment AD-KG, or vice versa, let alone the
potential for mutual enhancement between the two as we propose here.

##  3 Our Methodology

Figure 1: The overview pipeline of DALK. We first extract structural knowledge
from unstructured corpora and construct a domain-specific knowledge graph
tailored to AD (Section 3.1). Then, we utilize a coarse-to-fine sampling
method with a novel self-aware knowledge retrieval approach to select
appropriate knowledge from the knowledge graph (Section 3.2).

This section elaborates on our dynamic co-augmentation framework of LLMs and
KG. Section 3.1 presents the details of augmenting an AD-specific evolving KG
with LLMs and literature corpus in a time-slicing fashion (i.e. year by year).
Following it, Section 3.2 describes the process of sampling appropriate
knowledge from the evolving KG to enhance LLMs’ reasoning. Figure 1
illustrates an overall pipeline of our method DALK.

| $KG_{pair}$ | $KG_{gen}$
---|---|---
#Corpus | 9,764 | 9,764
#Nodes | 13,509 | 20,545
#Relations | 3,952 | 3,651
#Triples | 171,431 | 53,585
Table 1: Detailed statistics about our augmented knowledge graph.

###  3.1 LLMs for KG

##### Corpus Collection

To create an AD-specific knowledge graph, we follow Pu et al. (2023) and use
the AD corpus collected by a domain expert Professor Colin Masters at the
University of Melbourne who discovered amyloid proteins being the potential
cause of AD Masters et al. (1985). The corpus is based on his extensive
bibliography of representative AD-related papers and consists of more than 16K
PMID (PubMed ID)-indexed articles from 1977 to 2021. For our study, we focus
on the papers since 2011 which reflect the most recent knowledge in the field
and get 9,764 articles.

##### Entity Recognition

In order to identify knowledge at the proper granularity level for AD, we
extract relevant entities from the corpus by utilizing the PubTator Central
(PTC) Wei et al. (2013) developed and continuously maintained by NCBI. PTC is
a widely-used tool to provide state-of-the-art annotations of biomedical
concepts for PubMed abstracts and full-text articles, and it supports six
bioconcept types including genes, diseases, chemicals, mutations, species and
cell lines. We apply PTC to the abstracts of all our AD papers and obtain the
relevant named entities which will serve as nodes in the knowledge graph.

##### Relation Extraction

To build an accurate and high-quality knowledge graph on AD, we aim to assign
a specific relation type between the two related entities. Through a
comprehensive survey of relation extraction methods for knowledge graph
construction, we categorize current approaches with LLMs into two main groups:
(a). Pair-wised Relation Extraction Carta et al. (2023); Wadhwa et al. (2023)
aims to prompt the LLMs to describe the relationship between any two entities
in a segment of text. (b). Generative Relation Extraction Han et al. (2023);
Bi et al. (2024); Datta et al. (2024), where LLMs directly output all related
entity pairs and their corresponding relationships. As shown in Figure 2, we
incorporate both of these relation extraction methods into our knowledge graph
augmentation process to provide a comprehensive comparison between them. We
denote the resulting knowledge graphs from these approaches as $KG_{pair}$ and
$KG_{gen}$ respectively.

Table 1 presents the detailed statistics about our augmented knowledge graph,
including the number of corpora we used, and the number of nodes, relations
and triples in $KG_{pair}$ and $KG_{gen}$.

Figure 2: The detailed process of AD-specific KG construction.

###  3.2 KG for LLMs

In this section, we begin by outlining our process for sampling coarse-grained
augmented knowledge from our evolving knowledge graph (Section 3.2.1).
Subsequently, we delve into detail regarding our self-aware knowledge
retrieval method, which aims to filter out noise and retrieve the most
pertinent knowledge to provide to the LLM (Section 3.2.2).

####  3.2.1 Coarse-grained Knowledge Sample

Given a question query $Q$, we first construct a prompt and ask LLMs to
extract all the domain-specific entities $E=\\{e_{1},e_{2},...\\}$ from it.
Afterward, we adhere to the methodology proposed by Wen et al. (2023) and
execute a similarity-based entity linking process to connect all entities
within $E$ to the entity structure in our knowledge graph $G$. Specifically,
we employ a semantic similarity model Reimers and Gurevych (2019) to encode
all entities in $G$ and $E$ into dense embeddings, denoted as $H_{G}$ and
$H_{E}$, respectively. Subsequently, utilizing cosine similarity, we establish
links between each entity in $E$ and its nearest neighbor entity in $G$. This
procedure yields an initial entity set $E_{G}$ for the subsequent knowledge
sampling step.

To build an evidence sub-graph to boost LLMs’ reasoning process, we follow the
previous study Wen et al. (2023) and consider the following two kinds of
explorations in our AD-KG:

##### Path-based Exploration

entails the extraction of a sub-graph from $G$ to encompass all entities
within $E_{G}$. The process unfolds as follows: (a) Begin by selecting one
node from $e_{Q}^{0}$ as the initial node, denoted as $e_{1}$, and place the
remaining nodes into a candidate node set, $E_{cand}$. Explore at most $k$
hops from $e_{1}$ to identify the subsequent node, $e_{2}$, where $e_{1}\in
E_{cand}$. If $e_{2}$ is successfully reached within $k$ hops, update the
start node to $e_{2}$ and remove $e_{2}$ from $E_{cand}$. In the event $e_{2}$
cannot be found within $k$ hops, concatenate the segment paths acquired thus
far and store them in $G_{Q}^{path}$. Subsequently, choose another node
$e_{1}^{\prime}$ from $V_{cand}$ as the new start node, and eliminate both the
original start node $e_{1}$ and the current node $e_{2}$ from $E_{cand}$. (b)
Verify if $E_{cand}$ is empty. If not, repeat step (a) to identify the next
segment of the path. If $E_{cand}$ is empty, combine all segments to construct
a set of sub-graphs and place them into $G_{Q}^{path}$.

##### Neighbor-based Exploration

endeavors to augment the evidence relevant to the query within $G_{Q}$. This
process consists of two steps: (a) Initially, expand each node $e$ within
$E_{G}$ by 1-hop to incorporate their neighbors ${e^{\prime}}$, thus appending
triples ${(e,r,e^{\prime})}$ to $G_{Q}^{nei}$. (b) Then assess whether each
$e^{\prime}$ exhibits semantic relevance to the query. If affirmative, further
expand the 1-hop neighbors of $e^{\prime}$, consequently adding triples
$(e_{nei},r^{\prime},e^{\prime})$ to $G_{Q}^{nei}$.

After obtaining the two sub-graphs $G_{Q}^{path}$ and $G_{Q}^{nei}$, we
perform post-processing to further prune redundant information in sub-graphs
and prompt LLMs to describe the structure of each sub-graph.

####  3.2.2 Self-aware Knowledge Retrieval

In our initial experiment, we noticed the coarse-grained knowledge sampled
with the above-mentioned approaches still contained redundant and irrelevant
information. This issue of noise is a common challenge encountered in
automatically-constructed knowledge graphs Fang et al. (2021); Zhang et al.
(2020); Li et al. (2022); Bi et al. (2024). Moreover, many recent works Yu et
al. (2023); Li et al. (2023d); Chen et al. (2024); Wu et al. (2024) have
demonstrated LLMs can indeed be influenced by such noisy information. To
address this challenge, we borrow insights from the recent self-powered LLMs
Wang et al. (2022); Pan et al. (2023); Li et al. (2023a); Yuan et al. (2024);
Tong et al. (2024) and propose a self-aware knowledge retrieval method to
leverage LLMs’ ranking capability Sun et al. (2023b); Ma et al. (2023) to
filter out noisy information.

In particular, we directly prompt the LLM to rerank the sampled knowledge and
only retrieve top $k$ triples to provide for itself in the final-round
inference. Given the question $Q$ and either the path-based or neighbor-based
sub-graph $G_{Q}$, we create prompt $p_{self}$ by filling the pre-defined
template:

$p_{self}={\rm Template_{self}}(Q,G_{Q},k).$ (1)

Then, we use $p_{self}$ as the input to prompt the LLM to obtain the self-
retrieved knowledge:

$G_{Q}^{self}={\rm LLM}(p_{self}),$ (2)

Finally, we provide the question $Q$ and fine-grained knowledge $G_{Q}^{self}$
to the LLM for reasoning and get the predicted answer $a$ in two steps:

$p_{inference}={\rm Template_{inference}}(Q,G_{Q}^{self}),$ (3) $a={\rm
LLM}(p_{inference}).$ (4)

We provide detailed examples in Appendix A and B to demonstrate the input and
output in our DALK.

##  4 Main Experiment

###  4.1 ADQA Benchmark

For performance evaluation, we consider four widely-used medical QA datasets
spanning diverse biomedical topics Jin et al. (2021); Pal et al. (2022);
Hendrycks et al. (2021); Peñas et al. (2013) and derive an AD-specific QA
dataset from them. The four medical QA datasets are all multiple-choice based
and include: 1) MedQA Jin et al. (2021) consisting of US Medical Licensing
Examination (USMLE)-style questions, 2) MedMCQA Pal et al. (2022) containing
medical school entrance exam questions from India, 3) MMLU Hendrycks et al.
(2021) consisting of diverse biomedical and clinical questions from various
sources, 4) QA4MRE Peñas et al. (2013) containing a subset of questions for AD
derived from PubMed and Medline. In order to extract from the medical QA
datasets a subset of samples related to AD for our evaluation, we referred to
NIH’s Common Alzheimer’s and Related Dementias Research Ontology (CADRO)
111https://iadrp.nia.nih.gov/about/cadro. Jointly developed by the National
Institute on Aging and the Alzheimer’s Association, CADRO is a three-tiered
classification system with eight main categories and a dozen sub-categories
for AD and related dementia, and it contains common terminologies or keywords
used in the field. We derived from the CADRO a list of AD-related keywords
most relevant to the medical QA datasets: <Aging, Alzheimer, Amyloid beta,
APOE, Dementia, Lipoprotein, Microglia>. Then, we searched against each
medical QA dataset for matches with these keywords to find putative QA
samples, then further asked GPT-3.5-turbo to judge for each putative sample
whether the question is indeed related to AD or not. Finally, we filtered out
a subset of such samples that are considered highly relevant to AD to conduct
our evaluation (number of samples in each dataset is shown in Table 2). More
details about ADQA can be found in Appendix C.

###  4.2 Experiment Settings

We apply our framework with OpenAI GPT-3.5-turbo models OpenAI (2022). We also
include the following baseline methods for comparison:

##### Biomedical LLMs

Both ChatDoctor Yunxiang et al. (2023) and Med-Alpaca Shu et al. (2023) are
fine-tuned versions of LLaMA Touvron et al.  on biomedical corpora. Compared
with them, Meditron Chen et al. (2023) is built on LLaMA-2 Touvron et al.
(2023) and extends its pretraining on a comprehensively curated medical
corpus. BiomedGPT Zhang et al. (2023a) is also based on LLaMA-2 and pioneer as
the first open-source and generalist visual language AI for diverse biomedical
tasks. Biomistral Labrak et al. (2024) is an open-source LLM crafted
specifically for the biomedical domain, optimized for efficiency through
quantization and model merging techniques.

| MedQA | MedMCQA | MMLU | QA4MRE | AVG
---|---|---|---|---|---
_Biomedical LLMs_
ChatDoctor Yunxiang et al. (2023) | 25.7 | 36.4 | 46.9 | 51.4 | 40.1
Med-Alpaca Shu et al. (2023) | 41.4 | 42.8 | 44.9 | 57.1 | 46.5
BiomedGPT Zhang et al. (2023a) | 38.8 | 41.9 | 48.9 | 42.6 | 43.1
Meditron Chen et al. (2023) | 27.6 | 31.4 | 36.7 | 25.7 | 30.4
Biomistral Labrak et al. (2024) | 44.7 | 49.5 | 53.1 | 68.6 | 54.0
_Retrieval-augmented LLMs_
GPT-3.5-turbo w/ Ada OpenAI (2024) | 57.2 | 65.7 | 83.7 | 62.9 | 67.4
Almanac Zakka et al. (2024) | 48.0 | 69.5 | 71.4 | 60.0 | 62.2
Clinfo.ai Lozano et al. (2023) | 54.3 | 77.0 | 81.3 | 67.7 | 70.1
| Clinfo.ai w/o PubMed API
---
49.3 | 68.6 | 79.6 | 74.3 | 67.9
GPT-3.5-turbo | 50.0 | 71.9 | 83.6 | 62.9 | 67.1
DALK | 57.9 | 75.2 | 85.4 | 71.4 | 72.6
Table 2: Experiment results on our constructed ADQA benchmark. The sample size
is indicated after each dataset name. The best results of each metric are in
bold and the second-best results are underlined. The “AVG” column represents
the average accuracy score on the four sub-dataset.

##### Retrieval-Augmented LLMs

Furthermore, we also compare our method with several representative retrieval-
augmented LLMs in the biomedical domain. Almanac Zakka et al. (2024) is a
novel approach utilizing OpenAI’s GPT model integrated with a Qdrant vector
database to hold external sources of knowledge retrieved from local corpus,
web search, and calculators, designed to answer open-domain clinical
questions. Like Almanac, Lozano et al. (2023) introduced Clinfo.ai, which is
an open-source, end-to-end retrieval-augmented LLM (GPT) to answer medical
queries using scientific literature summarizations derived from PubMed search
engine. We adopt both Almanac and Clinfo.ai with the same prompt as ours to
answer multiple-choice questions to suit the ADQA benchmark. Lastly, we
implement a simple retrieval-augmented GPT baseline with CoT prompting similar
to our proposed DALK. All the GPT models used are set to GPT-3.5-turbo as
detailed in the next paragraph, to be consistent.

##### Implementation Details

We use the knowledge graph constructed with the generative approach
($KG_{gen}$) in our main experiment and conduct an ablation study on the
knowledge graph with RE method ($KG_{pair}$) in Section 4.5. We use
GPT-3.5-turbo with the version “gpt-3.5-turbo-0301” and set the sampling
temperature to 0.7. We utilize 7B versions of all the biomedical LLMs
baselines. For RAG methods, we split each document with a max length of 128
and retrieve the top 3 most relevant documents as the support evidence for
LLMs to do inference. We set the parameter $k$ in our self-aware knowledge
retrieval to 5 and conduct further analysis on it in Section 5.2.

###  4.3 Main Result

Table 2 shows the experimental results on our ADQA benchmark. We note that
upon applying our dynamic co-augmentation framework, DALK’s performance
surpasses that of other biomedical LLMs and RAG methods overall. It
consistently achieves either the best or the second-best accuracy score across
all sub-datasets and attains the highest AVG score. Furthermore, the
substantial improvement over vanilla GPT-3.5-turbo underscores the efficacy of
our approach in domain-specific ADQA.

Furthermore, we observe that the performance of biomedical-specific LLMs
generally lags behind that of GPT-3.5-turbo. We attribute this discrepancy to
the smaller size of these biomedical LLMs. While they may perform adequately
in general medical contexts, they fall short in the AD scenario, which demands
more domain-specific knowledge. In the case of GPT-3.5-turbo combined with
various RAG methods, it is evident that most RAG methods enhance the models’
performance. Among them, GPT-3.5-turbo with Clinfo.ai yields the most
significant improvement, boosting the accuracy score from 67.1 to 70.1
compared to vanilla GPT-3.5-turbo. However, it is important to note that the
original Clinfo.ai necessitates access to the PubMed API, constituting an
external resource. When we disable this access and solely utilize the same
corpora as in DALK within the Clinfo.ai retrieval system, the improvement it
brings becomes marginal and incomparable to our method. Due to the space
limitation, we put more RAG results with different hyper-parameters in
Appendix D.

###  4.4 Ablation Study on Self-aware Knowledge Retrieval

| MedQA | MedMCQA | MMLU | QA4MRE | AVG
---|---|---|---|---|---
AVG Length | 107.4 | 23.8 | 342.9 | 17.6 | 122.9
GPT-3.5-turbo | 50.0 | 71.9 | 83.6 | 62.9 | 67.1
DALK | 57.9 | 75.2 | 85.4 | 71.4 | 72.6
| DALK
---
w/o self-aware
knowledge retrieval
56.5 | 71.0 | 77.6 | 77.1 | 70.6
Table 3: Ablation study results with and without our proposed self-aware
knowledge retrieval.

In this section, we evaluate the efficacy of our proposed self-aware knowledge
retrieval method through an ablation study. As depicted in Table 3, we observe
that while the dynamic co-augmentation framework without the self-aware
knowledge retrieval module still enhances the model’s performance, the overall
improvement is less pronounced. Furthermore, we observe that the efficacy of
self-aware knowledge retrieval correlates with the length of queries within a
given context. For instance, a notable enhancement in performance is evident
within the MMLU sub-dataset upon the implementation of self-aware knowledge
retrieval. We attribute this to the fact that questions in the MMLU dataset
typically contain longer contexts compared to other medical QA datasets
integrated into ADQA. Consequently, irrelevant knowledge sourced from the
context may exacerbate the issue of information noise thus underscoring the
necessity for self-aware retrieval. Conversely, within QA4MRE, characterized
by shorter query lengths, the application of self-aware knowledge retrieval
can even lead to a decline in performance.

###  4.5 Ablation Study on KG Construction

| AVG | #Triples
---|---|---
GPT-3.5-turbo | 67.1 | -
| DALK
---
w/ Generative KG
72.6 | 53,585
| DALK
---
w/ RE KG
66.3 | 171,431
Table 4: Ablation study results with generative construction and RE
construction.

Table 4 illustrates the results of the ablation study conducted using
generatively constructed KG and RE-constructed KG. Surprisingly, despite the
RE method yielding a KG with a larger scale and more triples, knowledge
sampled from it has unexpectedly resulted in a non-trivial drop in performance
within ADQA. After a manual examination of the two constructed knowledge
graphs, we find LLMs with the RE construction method have a strong inclination
to wrongly assign a relationship to two unrelated entities, which has been
exposed by the previous studies Wan et al. (2023). In contrast, the generative
construction approach exclusively outputs triples that LLMs confidently
endorse, yielding a smaller yet more precise knowledge graph. This trade-off
between coverage and accuracy underscores the critical importance of denoising
in the construction of KGs by LLMs.

##  5 Further Analysis

###  5.1 Co-augmentation Analysis

Figure 3: The size of the knowledge graph (triplet number) and the KG-
augmented GPT-3.5-turbo’s performance (accuracy) over time.

To comprehensively understand how the performance of LLMs evolves in response
to increasing KG sizes, we undertake a detailed co-augmentation analysis.
Illustrated in Figure 3, our experiments aim to discern the changing
performance trends of LLMs as the knowledge triples accumulate annually. Our
findings reveal that our framework effectively fosters the co-evolution of
LLMs and KG, with the performance of KG-augmented LLMs exhibiting a generally
upward trajectory as the KG expands. Notably, when we remove the self-aware
knowledge retrieval module, this upward trend becomes less significant. This
further implies the importance of sampling and selecting appropriate knowledge
for LLMs when the KG’s size increases.

| Path-based Sub-graph | Answer
---|---|---
Baseline | - | C ✗
| DALK
---
-w/o self-aware
knowledge retrieval
| neurofibrillary tangles->FORM BY->microtubule-associated protein tau…
---
…
entorhinal cortex->is a part of->brain->ASSOCIATES->mouse with
Alzheimer’s disease->brain region->temporal lobe
C ✗
DALK |  | Reranked Triples1: entorhinal cortex ->is a part of ->brain
---
Reranked Triples2: entorhinal cortex ->associates ->mouse with Alzheimer’s
disease
Reranked Triples3: temporal lobe ->affected by ->Alzheimer’s disease
D ✓
Table 5: A case to show the effectiveness of DALK. The question is: “The area
of the brain resistant to Neurofibrillary tangles of Alzheimer’s disease is:
A. Visual association areas B. Entorhinal coex C. Temporal lobe D.Lateral
geniculate body”

###  5.2 Hyper-parameter Analysis

Figure 4: Different self-aware knowledge retrieval settings in MedQA, MedMCQA,
QA4MRE and the average score (AVG).

In this section, we do a hyper-parameter analysis on the retrieval number $k$
of our self-aware retrieval module. We select a group of value for $k$
([1,3,5,10,20,30]) and present the experiment results in Figure 4. We show the
accuracy score on MedQA, MedMCQA, QA4MRE and AVG with different $k$. We find
when $k$ is small, an increment to it can lead to a performance enhancement.
After the best performance shows up, continually increasing the value of $k$
will cause a smooth decrease in the model accuracy score. This result
indicates the knowledge ranked in the top positions is more helpful while the
knowledge ranked behind is something not very useful, thus successfully
validating the capability of LLMs to do a fine-grained knowledge reranking.

Moreover, we find the best $k$ value is correlated with the length of queries
in each single dataset. For example, the best performance in MedQA (average
query length is 107.4) shows up when $k=10$ while the best performance in
MedMCQA and QA4MRE shows up when $k=5$ and $3$ respectively. This is
consistent with our findings in Section 4.4 that a longer query corresponds to
a larger and noisier sub-knowledge graph.

###  5.3 Sensitivity Analysis on ADQA Benchmark

Benchmark | DALK |  | DALK
---
w/o self-aware
knowledge retrieval
ADQA | 72.6 | 70.6
w/o “Alzheimer” | 72.1 | 70.4
w/o “Dementia” | 72.4 | 71.3
w/o “APOE” | 73.2 | 71.2
w/o “Amyloid beta” | 73.5 | 70.7
w/o “Aging” | 72.9 | 71.4
w/o “Lipoprotein” | 73.1 | 71.0
w/o “Microglia” | 72.8 | 70.9
Table 6: Sensitivity analysis for ADQA benchmark with a leave-one-out
evaluation on AD-related keywords.

In this section, we conduct a sensitivity analysis for our constructed ADQA by
conducting a leave-one-out evaluation on AD-related keywords. We do it by
removing the samples with each keyword in our keyword list and calculating the
AVG score of the remaining samples. As the result shown in Table 6, we find
not all of the keywords are incorporated in our ADQA benchmark. Notably, the
keywords “CSF Biomarkers”, “Neurogenesis”, “PET Amyloid”, “PET Tau”, “Tau
Phosphorylation” lack corresponding samples in ADQA. We believe one critical
work in the future for benchmarking AD-related knowledge is to collect QA
samples to cover these missing keywords. Moreover, analyzing the performance
variation upon removing samples linked to each keyword offers insight into
determining the relevance of the keyword to AD.

###  5.4 Case Study

We put an example in Table 5 to showcase the efficacy of DALK. We notice while
the path-based sub-graph contains the relevant knowledge to exclude option C,
it still involves other irrelevant information and finally fails to prompt the
LLMs to produce the correct answer. In contrast, our self-aware knowledge
retrieval method successfully chooses the top 3 most relevant triples for the
given problem and results in the correct answer D.

##  6 Conclusion

In this research, we begin by analyzing the main limitations of adopting the
existing LLMs-based methods in AD-specific areas. To address these issues, we
propose a novel approach in the merging of large language models and knowledge
graphs in the context of Alzheimer’s Disease. Our team provides an innovative
dynamic co-augmentation framework for the refinement of large language models
and knowledge graphs. Initially, our approach extracts structural insights
from the unstructured scientific literature, crafting a specialized knowledge
graph for AD. Subsequently, we employ a coarse-to-fine sampling technique
coupled with a unique self-aware knowledge retrieval strategy to pinpoint
relevant information from the knowledge graph. The extensive evaluation
conducted in our constructed ADQA benchmark showcases the effectiveness of our
method and provides further hints into the synergy of LLMs and knowledge graph
in the context of AD. In the future, we will do more exploration in adopting
and benchmarking LLMs in the AD areas.

##  7 Limitations

In the development of our AD-KG, our primary focus lies in the exploration of
two distinct methods for extracting relationships between associated entities.
For entity recognition, we employ a strong PubTator annotator directly,
without delving into the utilization of LLMs in this context. However, we have
observed that LLMs also exhibit promising entity extraction capabilities in
Section 3.2.1. We defer the refinement of methods for extracting entities for
KG construction with LLMs to future works. Furthermore, a significant
contribution of our work is the establishment of the ADQA benchmark.
Nonetheless, the datasets utilized in constructing ADQA primarily consist of
medical school exam questions, potentially exhibiting a domain gap from the
scientific literature informing AD-KG. One potential remedy is leveraging
PubmedQA Jin et al. (2019); however, it is hindered by limited data amount. In
the future, we will keep gathering AD-related QA samples and expanding the
size of our ADQA benchmark.

##  8 Ethics Statement

We have familiarized ourselves with and honour the ethical code set out in the
ACL Code of Ethics222https://www.aclweb.org/portal/content/acl-code-ethics.
The knowledge graphs constructed in the paper are based on published
scientific literature from PubMed. The ADQA dataset used in the study is also
derived from publicly available medical QA datasets that are properly cited.
We strive to ensure our study upholds ethical principles and not cause any
kind of safety or privacy concerns. Although not observed in our multiple-
choice QA analysis, we recognize the possibility of factual errors and
hallucinations when using pre-trained LLMs for medical QA tasks in general,
and we do not recommend these models be applied in a practical setting at
present.

## References

  * Agbavor and Liang (2022) Felix Agbavor and Hualou Liang. 2022.  Predicting dementia from spontaneous speech using large language models.  _PLOS Digital Health_ , 1:1–14.
  * Anil et al. (2023) Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al. 2023.  Palm 2 technical report.  _arXiv preprint arXiv:2305.10403_.
  * Balagopalan et al. (2020) Aparna Balagopalan, Benjamin Eyre, Frank Rudzicz, and Jekaterina Novikova. 2020.  To BERT or not to BERT: Comparing Speech and Language-Based Approaches for Alzheimer’s Disease Detection.  In _Proc. Interspeech 2020_ , pages 2167–2171.
  * Besta et al. (2023) Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, et al. 2023.  Graph of thoughts: Solving elaborate problems with large language models.  _arXiv preprint arXiv:2308.09687_.
  * Bi et al. (2024) Zhen Bi, Jing Chen, Yinuo Jiang, Feiyu Xiong, Wei Guo, Huajun Chen, and Ningyu Zhang. 2024.  Codekgc: Code language model for generative knowledge graph construction.  _ACM Transactions on Asian and Low-Resource Language Information Processing_ , 23(3):1–16.
  * Brown et al. (2020) Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020.  Language models are few-shot learners.  _Advances in neural information processing systems_ , 33:1877–1901.
  * Carta et al. (2023) Salvatore Carta, Alessandro Giuliani, Leonardo Piano, Alessandro Sebastian Podda, Livio Pompianu, and Sandro Gabriele Tiddia. 2023.  Iterative zero-shot llm prompting for knowledge graph construction.  _arXiv preprint arXiv:2307.01128_.
  * Chen et al. (2024) Jiawei Chen, Hongyu Lin, Xianpei Han, and Le Sun. 2024.  Benchmarking large language models in retrieval-augmented generation.  In _Proceedings of the AAAI Conference on Artificial Intelligence_ , volume 38, pages 17754–17762.
  * Chen et al. (2023) Zeming Chen, Alejandro Hernández Cano, Angelika Romanou, Antoine Bonnet, Kyle Matoba, Francesco Salvi, Matteo Pagliardini, Simin Fan, Andreas Köpf, Amirkeivan Mohtashami, et al. 2023.  Meditron-70b: Scaling medical pretraining for large language models.  _arXiv preprint arXiv:2311.16079_.
  * Daluwatumulle et al. (2023) Geesa Daluwatumulle, Rupika Wijesinghe, and Ruvan Weerasinghe. 2023.  In silico drug repurposing using knowledge graph embeddings for alzheimer’s disease.  In _Proceedings of the 9th International Conference on Bioinformatics Research and Applications_ , ICBRA ’22, page 61–66, New York, NY, USA. Association for Computing Machinery.
  * Datta et al. (2024) Preetha Datta, Fedor Vitiugin, Anastasiia Chizhikova, and Nitin Sawhney. 2024.  Construction of hyper-relational knowledge graphs using pre-trained large language models.  _arXiv preprint arXiv:2403.11786_.
  * Dernbach et al. (2024) Stefan Dernbach, Khushbu Agarwal, Alejandro Zuniga, Michael Henry, and Sutanay Choudhury. 2024.  Glam: Fine-tuning large language models for domain knowledge graph alignment via neighborhood partitioning and generative subgraph encoding.  _arXiv preprint arXiv:2402.06764_.
  * Fang et al. (2021) Tianqing Fang, Hongming Zhang, Weiqi Wang, Yangqiu Song, and Bin He. 2021.  Discos: bridging the gap between discourse knowledge and commonsense knowledge.  In _Proceedings of the Web Conference 2021_ , pages 2648–2659.
  * Feng et al. (2023) Yingjie Feng, Xiaoyin Xu, Yueting Zhuang, and Min Zhang. 2023.  Large language models improve alzheimer’s disease diagnosis using multi-modality data.  In _2023 IEEE International Conference on Medical Artificial Intelligence (MedAI)_ , pages 61–66. IEEE.
  * Han et al. (2023) Jiuzhou Han, Nigel Collier, Wray Buntine, and Ehsan Shareghi. 2023.  Pive: Prompting with iterative verification improving graph-based generative capability of llms.  _arXiv preprint arXiv:2305.12392_.
  * Hendrycks et al. (2021) Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021.  Measuring massive multitask language understanding.  In _International Conference on Learning Representations_.
  * Himmelstein et al. (2017) Daniel Scott Himmelstein, Antoine Lizee, Christine Hessler, Leo Brueggeman, Sabrina L Chen, Dexter Hadley, Ari Green, Pouya Khankhanian, and Sergio E Baranzini. 2017.  Systematic integration of biomedical knowledge prioritizes drugs for repurposing.  _Elife_ , 6:e26726.
  * Hsieh et al. (2023) Kang-Lin Hsieh, German Plascencia-Villa, Ko-Hong Lin, George Perry, Xiaoqian Jiang, and Yejin Kim. 2023.  Synthesize heterogeneous biological knowledge via representation learning for alzheimer’s disease drug repurposing.  _iScience_ , 26(1):105678.
  * Hu et al. (2021) Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. 2021.  Lora: Low-rank adaptation of large language models.  In _International Conference on Learning Representations_.
  * Jiang et al. (2023) Jinhao Jiang, Kun Zhou, Zican Dong, Keming Ye, Wayne Xin Zhao, and Ji-Rong Wen. 2023.  Structgpt: A general framework for large language model to reason over structured data.  In _Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing_ , pages 9237–9251.
  * Jin et al. (2021) Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. 2021.  What disease does this patient have? a large-scale open domain question answering dataset from medical exams.  _Applied Sciences_ , 11(14):6421.
  * Jin et al. (2019) Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W Cohen, and Xinghua Lu. 2019.  Pubmedqa: A dataset for biomedical research question answering.  _arXiv preprint arXiv:1909.06146_.
  * Kandpal et al. (2023) Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, and Colin Raffel. 2023.  Large language models struggle to learn long-tail knowledge.  In _International Conference on Machine Learning_ , pages 15696–15707. PMLR.
  * Kim et al. (2023) Jiho Kim, Yeonsu Kwon, Yohan Jo, and Edward Choi. 2023.  Kg-gpt: A general framework for reasoning on knowledge graphs using large language models.  In _Findings of the Association for Computational Linguistics: EMNLP 2023_ , pages 9410–9421.
  * Labrak et al. (2024) Yanis Labrak, Adrien Bazoge, Emmanuel Morin, Pierre-Antoine Gourraud, Mickael Rouvier, and Richard Dufour. 2024.  Biomistral: A collection of open-source pretrained large language models for medical domains.  _arXiv preprint arXiv:2402.10373_.
  * Li et al. (2022) Dawei Li, Yanran Li, Jiayi Zhang, Ke Li, Chen Wei, Jianwei Cui, and Bin Wang. 2022.  C3kg: A chinese commonsense conversation knowledge graph.  In _Findings of the Association for Computational Linguistics: ACL 2022_ , pages 1369–1383.
  * Li et al. (2023a) Dawei Li, Yaxuan Li, Dheeraj Mekala, Shuyao Li, Xueqi Wang, William Hogan, Jingbo Shang, et al. 2023a.  Dail: Data augmentation for in-context learning via self-paraphrase.  _arXiv preprint arXiv:2311.03319_.
  * Li et al. (2024) Dawei Li, Zhen Tan, Tianlong Chen, and Huan Liu. 2024.  Contextualization distillation from large language model for knowledge graph completion.  _arXiv preprint arXiv:2402.01729_.
  * Li et al. (2023b) Dawei Li, Hengyuan Zhang, Yanran Li, and Shiping Yang. 2023b.  Multi-level contrastive learning for script-based character understanding.  In _Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing_ , pages 5995–6013.
  * Li et al. (2023c) Rumeng Li, Xun Wang, and Hong Yu. 2023c.  Two directions for clinical data generation with large language models: Data-to-label and label-to-data.  In _Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing_ , pages 7129–7143.
  * Li et al. (2023d) Yucheng Li, Bo Dong, Frank Guerin, and Chenghua Lin. 2023d.  Compressing context to enhance inference efficiency of large language models.  In _Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing_ , pages 6342–6353.
  * Lozano et al. (2023) Alejandro Lozano, Scott L Fleming, Chia-Chun Chiang, and Nigam Shah. 2023.  Clinfo. ai: An open-source retrieval-augmented large language model system for answering medical questions using scientific literature.  In _PACIFIC SYMPOSIUM ON BIOCOMPUTING 2024_ , pages 8–23. World Scientific.
  * Luo et al. (2024) Xindi Luo, Zequn Sun, Jing Zhao, Zhe Zhao, and Wei Hu. 2024.  Knowla: Enhancing parameter-efficient finetuning with knowledgeable adaptation.  _arXiv preprint arXiv:2403.14950_.
  * Ma et al. (2023) Yubo Ma, Yixin Cao, Yong Hong, and Aixin Sun. 2023.  Large language model is not a good few-shot information extractor, but a good reranker for hard samples!  In _Findings of the Association for Computational Linguistics: EMNLP 2023_ , pages 10572–10601.
  * Mao et al. (2023) Chengsheng Mao, Jie Xu, Luke Rasmussen, Yikuan Li, Prakash Adekkanattu, Jennifer Pacheco, Borna Bonakdarpour, Robert Vassar, Li Shen, Guoqian Jiang, Fei Wang, Jyotishman Pathak, and Yuan Luo. 2023.  Ad-bert: Using pre-trained language model to predict the progression from mild cognitive impairment to alzheimer’s disease.  _Journal of Biomedical Informatics_ , 144:104442.
  * Masters et al. (1985) C L Masters, G Simms, N A Weinman, G Multhaup, B L McDonald, and K Beyreuther. 1985.  Amyloid plaque core protein in alzheimer disease and down syndrome. _Proceedings of the National Academy of Sciences_ , 82(12):4245–4249.
  * Miller (1995) George A Miller. 1995.  Wordnet: a lexical database for english.  _Communications of the ACM_ , 38(11):39–41.
  * Nian et al. (2022) Yi Nian, Xinyue Hu, Rui Zhang, Jingna Feng, Jingcheng Du, Fang Li, Yong Chen, and Cui Tao. 2022.  Mining on alzheimer’s diseases related knowledge graph to identity potential ad-related semantic triples for drug repurposing.  _BMC Bioinformatics_ , 23.
  * OpenAI (2022) OpenAI. 2022.  Introducing chatgpt.
  * OpenAI (2024) OpenAI. 2024.  New embedding models and api updates.
  * Ovadia et al. (2023) Oded Ovadia, Menachem Brief, Moshik Mishaeli, and Oren Elisha. 2023.  Fine-tuning or retrieval? comparing knowledge injection in llms.  _arXiv preprint arXiv:2312.05934_.
  * Pal et al. (2022) Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. 2022.  Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering.  In _Proceedings of the Conference on Health, Inference, and Learning_ , volume 174 of _Proceedings of Machine Learning Research_ , pages 248–260. PMLR.
  * Pan et al. (2023) Liangming Pan, Michael Saxon, Wenda Xu, Deepak Nathani, Xinyi Wang, and William Yang Wang. 2023.  Automatically correcting large language models: Surveying the landscape of diverse self-correction strategies.  _arXiv preprint arXiv:2308.03188_.
  * Pan et al. (2024) Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. 2024.  Unifying large language models and knowledge graphs: A roadmap.  _IEEE Transactions on Knowledge and Data Engineering_.
  * Peñas et al. (2013) Anselmo Peñas, Eduard H. Hovy, Pamela Forner, Álvaro Rodrigo, Richard F. E. Sutcliffe, and Roser Morante. 2013.  Qa4mre 2011-2013: Overview of question answering for machine reading evaluation.  In _International Conference of the Cross-Language Evaluation Forum for European Languages_ , pages 303–320. Springer.
  * Pu et al. (2023) Yiyuan Pu, Daniel Beck, and Karin Verspoor. 2023.  Graph embedding-based link prediction for literature-based discovery in alzheimer’s disease.  _Journal of Biomedical Informatics_ , 145:104464.
  * Rangel et al. (2024) Julio C Rangel, Tarcisio Mendes de Farias, Ana Claudia Sima, and Norio Kobayashi. 2024.  Sparql generation: an analysis on fine-tuning openllama for question answering over a life science knowledge graph.  _arXiv preprint arXiv:2402.04627_.
  * Reimers and Gurevych (2019) Nils Reimers and Iryna Gurevych. 2019.  Sentence-bert: Sentence embeddings using siamese bert-networks.  In _Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)_ , pages 3982–3992.
  * Report (2023) Alzheimer’s Association Report. 2023.  2023 alzheimer’s disease facts and figures.  _Alzheimer’s & Dementia_, 19(4):1598–1695.
  * Romano et al. (2023) Joseph Romano, Van Truong, Rachit Kumar, Mythreye Venkatesan, Britney Graham, Yun Hao, Nick Matsumoto, Xi Li, Zhiping Wang, Marylyn Ritchie, Li Shen, and Jason Moore. 2023.  The alzheimer’s knowledge base – a knowledge graph for therapeutic discovery in alzheimer’s disease research (preprint).  _Journal of Medical Internet Research_.
  * Shu et al. (2023) Chang Shu, Baian Chen, Fangyu Liu, Zihao Fu, Ehsan Shareghi, and Nigel Collier. 2023.  Visual med-alpaca: A parameter-efficient biomedical llm with visual capabilities.
  * Speer et al. (2017) Robyn Speer, Joshua Chin, and Catherine Havasi. 2017.  Conceptnet 5.5: An open multilingual graph of general knowledge.  In _Proceedings of the AAAI conference on artificial intelligence_ , volume 31.
  * Sun et al. (2023a) Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Heung-Yeung Shum, and Jian Guo. 2023a.  Think-on-graph: Deep and responsible reasoning of large language model with knowledge graph.  _arXiv preprint arXiv:2307.07697_.
  * Sun et al. (2023b) Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, and Zhaochun Ren. 2023b.  Is chatgpt good at search? investigating large language models as re-ranking agents.  In _Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing_ , pages 14918–14937.
  * Tan et al. (2024) Zhen Tan, Alimohammad Beigi, Song Wang, Ruocheng Guo, Amrita Bhattacharjee, Bohan Jiang, Mansooreh Karami, Jundong Li, Lu Cheng, and Huan Liu. 2024.  Large language models for data annotation: A survey.  _arXiv preprint arXiv:2402.13446_.
  * Tang et al. (2023) Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Lixin Su, Suqi Cheng, Dawei Yin, and Chao Huang. 2023.  Graphgpt: Graph instruction tuning for large language models.  _arXiv preprint arXiv:2310.13023_.
  * Tong et al. (2024) Yongqi Tong, Dawei Li, Sizhe Wang, Yujia Wang, Fei Teng, and Jingbo Shang. 2024.  Can llms learn from previous mistakes? investigating llms’ errors to boost for reasoning.  _arXiv preprint arXiv:2403.20046_.
  * Tong et al. (2023) Yongqi Tong, Yifan Wang, Dawei Li, Sizhe Wang, Zi Lin, Simeng Han, and Jingbo Shang. 2023.  Eliminating reasoning via inferring with planning: A new framework to guide llms’ non-linear thinking.  _arXiv preprint arXiv:2310.12342_.
  * (59) Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothee Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al.  Llama: Open and efficient foundation language models.
  * Touvron et al. (2023) Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023.  Llama 2: Open foundation and fine-tuned chat models.  _arXiv preprint arXiv:2307.09288_.
  * Vrandečić and Krötzsch (2014) Denny Vrandečić and Markus Krötzsch. 2014.  Wikidata: a free collaborative knowledgebase.  _Communications of the ACM_ , 57(10):78–85.
  * Wadhwa et al. (2023) Somin Wadhwa, Silvio Amir, and Byron C Wallace. 2023.  Revisiting relation extraction in the era of large language models.  In _Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_ , pages 15566–15589.
  * Wan et al. (2023) Zhen Wan, Fei Cheng, Zhuoyuan Mao, Qianying Liu, Haiyue Song, Jiwei Li, and Sadao Kurohashi. 2023.  Gpt-re: In-context learning for relation extraction using large language models.  In _Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing_ , pages 3534–3547.
  * Wang et al. (2022) Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022.  Self-consistency improves chain of thought reasoning in language models.  In _The Eleventh International Conference on Learning Representations_.
  * Wei et al. (2013) Chih-Hsuan Wei, Hung-Yu Kao, and Zhiyong Lu. 2013.  Pubtator: a web-based text mining tool for assisting biocuration.  _Nucleic acids research_ , 41(W1):W518–W522.
  * Wei et al. (2022) Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022.  Chain-of-thought prompting elicits reasoning in large language models.  _Advances in Neural Information Processing Systems_ , 35:24824–24837.
  * Wei et al. (2023) Yanbin Wei, Qiushi Huang, Yu Zhang, and James Kwok. 2023.  Kicgpt: Large language model with knowledge in context for knowledge graph completion.  In _Findings of the Association for Computational Linguistics: EMNLP 2023_ , pages 8667–8683.
  * Wen et al. (2023) Yilin Wen, Zifeng Wang, and Jimeng Sun. 2023.  Mindmap: Knowledge graph prompting sparks graph of thoughts in large language models.  _arXiv preprint arXiv:2308.09729_.
  * Wu et al. (2024) Siye Wu, Jian Xie, Jiangjie Chen, Tinghui Zhu, Kai Zhang, and Yanghua Xiao. 2024.  How easily do irrelevant inputs skew the responses of large language models?  _arXiv preprint arXiv:2404.03302_.
  * Yan et al. (2024) Chao Yan, Monika Grabowska, Alyson Dickson, Bingshan Li, Zhexing Wen, Dan Roden, C. Stein, Peter Embí, Josh Peterson, Qiping Feng, Bradley Malin, and Wei-Qi Wei. 2024.  Leveraging generative ai to prioritize drug repurposing candidates for alzheimer’s disease with real-world clinical validation.  _npj Digital Medicine_ , 7.
  * Yao et al. (2023) Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan. 2023.  Tree of thoughts: Deliberate problem solving with large language models.  _arXiv preprint arXiv:2305.10601_.
  * Yu et al. (2023) Wenhao Yu, Hongming Zhang, Xiaoman Pan, Kaixin Ma, Hongwei Wang, and Dong Yu. 2023.  Chain-of-note: Enhancing robustness in retrieval-augmented language models.  _arXiv preprint arXiv:2311.09210_.
  * Yuan et al. (2024) Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, Sainbayar Sukhbaatar, Jing Xu, and Jason Weston. 2024.  Self-rewarding language models.  _arXiv preprint arXiv:2401.10020_.
  * Yunxiang et al. (2023) Li Yunxiang, Li Zihan, Zhang Kai, Dan Ruilong, and Zhang You. 2023.  Chatdoctor: A medical chat model fine-tuned on llama model using medical domain knowledge.  _arXiv preprint arXiv:2303.14070_.
  * Zakka et al. (2024) Cyril Zakka, Rohan Shad, Akash Chaurasia, Alex R Dalal, Jennifer L Kim, Michael Moor, Robyn Fong, Curran Phillips, Kevin Alexander, Euan Ashley, et al. 2024.  Almanac—retrieval-augmented language models for clinical medicine.  _NEJM AI_ , 1(2):AIoa2300068.
  * Zhang et al. (2024) Hengyuan Zhang, Yanru Wu, Dawei Li, Zacc Yang, Rui Zhao, Yong Jiang, and Fei Tan. 2024.  Balancing speciality and versatility: a coarse to fine framework for supervised fine-tuning large language model.  _arXiv preprint arXiv:2404.10306_.
  * Zhang et al. (2020) Hongming Zhang, Xin Liu, Haojie Pan, Yangqiu Song, and Cane Wing-Ki Leung. 2020.  Aser: A large-scale eventuality knowledge graph.  In _Proceedings of the web conference 2020_ , pages 201–211.
  * Zhang et al. (2023a) Kai Zhang, Jun Yu, Zhiling Yan, Yixin Liu, Eashan Adhikarla, Sunyang Fu, Xun Chen, Chen Chen, Yuyin Zhou, Xiang Li, et al. 2023a.  Biomedgpt: A unified and generalist biomedical generative pre-trained transformer for vision, language, and multimodal tasks.  _arXiv preprint arXiv:2305.17100_.
  * Zhang et al. (2022) Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. 2022.  Opt: Open pre-trained transformer language models.  _arXiv preprint arXiv:2205.01068_.
  * Zhang et al. (2023b) Yichi Zhang, Zhuo Chen, Wen Zhang, and Huajun Chen. 2023b.  Making large language models perform better in knowledge graph completion.  _arXiv preprint arXiv:2310.06671_.

##  Appendix A Details of LLMs for KG

Table 7 and 8 present examples of our two KG construction methods
respectively. For both methods, we adopt a select-or-generate prompt to
instruct the LLM whether to choose a relation from hetionet Himmelstein et al.
(2017), a well-built general medical KG, or generate a new one to describe the
relationship between two entities. In the RE construction method, we also
conduct a type matching (Table 9) for each entity from type name of PubTator
to that of Hetionet and ask the LLM to choose from the relation set that
corresponds to the two entities’ types (Table 10).

Input |  Read the following abstract, extract the relationships between each entity.You can choose the relation from: (covaries, interacts, regulates, resembles, downregulates, upregulates, associates, binds, treats, palliates), or generate a new predicate to describe the relationship between the two entities. Output all the extract triples in the format of "head | relation | tail". For example: "Alzheimer’s disease | associates | memory deficits" Abstract: Thiamine pyrophosphate (TPP) and the activities of thiamine-dependent enzymes are reduced in Alzheimer’s disease (AD) patients. In this study, we analyzed the relationship between thiamine deficiency (TD) and amyloid precursor protein (APP) processing in both cellular and animal models of TD. In SH-SY5Y neuroblastoma cells overexpressing APP, TD promoted maturation of beta-site APP cleaving enzyme 1 (BACE1) and increased beta-secretase activity which resulted in elevated levels of beta-amyloid (Abeta) as well as beta-secretase cleaved C-terminal fragment (beta-CTF). An inhibitor of beta-secretase efficiently reduced TD-induced up-regulation of Abeta and beta-CTF. Importantly, thiamine supplementation reversed the TD-induced alterations. Furthermore, TD treatment caused a significant accumulation of reactive oxygen species (ROS); antioxidants suppressed ROS production and maturation of BACE1, as well as TD-induced Abeta accumulation. On the other hand, exogenous Abeta(1-40) enhanced TD-induced production of ROS. A study on mice indicated that TD also caused Abeta accumulation in the brain, which was reversed by thiamine supplementation. Taken together, our study suggests that TD could enhance Abeta generation by promoting beta-secretase activity, and the accumulation of Abeta subsequently exacerbated TD-induced oxidative stress.Entity: Thiamine (thiamine), deficiency increases beta-secretase, TPP (Thiamine pyrophosphate), Alzheimer’s disease (AD), patients, amyloid precursor protein (beta-amyloid (Abeta)), neuroblastoma, BACE1 (beta-site APP cleaving enzyme 1), Abeta, ROS (reactive oxygen species), Abeta accumulation, mice. Output:
---|---
Output |  Thiamine deficiency | downregulates | TPP, Thiamine deficiency | regulates | Alzheimer’s disease, Alzheimer’s disease | involves | beta-secretase, Amyloid precursor protein | produces | beta-amyloid, Amyloid precursor protein | in | neuroblastoma, BACE1 | is | beta-secretase, BACE1 | regulates | beta-amyloid, Abeta | is | beta-amyloid, Abeta | in | mice, Abeta | causes | reactive oxygen species, Abeta | regulates | ROS
Table 7: An example to extract knowledge triples using generative construction method. Input |  Read the following abstract, answer the following question. Abstract: Thiamine pyrophosphate (TPP) and the activities of thiamine-dependent enzymes are reduced in Alzheimer’s disease (AD) patients. In this study, we analyzed the relationship between thiamine deficiency (TD) and amyloid precursor protein (APP) processing in both cellular and animal models of TD. In SH-SY5Y neuroblastoma cells overexpressing APP, TD promoted maturation of beta-site APP cleaving enzyme 1 (BACE1) and increased beta-secretase activity which resulted in elevated levels of beta-amyloid (Abeta) as well as beta-secretase cleaved C-terminal fragment (beta-CTF). An inhibitor of beta-secretase efficiently reduced TD-induced up-regulation of Abeta and beta-CTF. Importantly, thiamine supplementation reversed the TD-induced alterations. Furthermore, TD treatment caused a significant accumulation of reactive oxygen species (ROS); antioxidants suppressed ROS production and maturation of BACE1, as well as TD-induced Abeta accumulation. On the other hand, exogenous Abeta(1-40) enhanced TD-induced production of ROS. A study on mice indicated that TD also caused Abeta accumulation in the brain, which was reversed by thiamine supplementation. Taken together, our study suggests that TD could enhance Abeta generation by promoting beta-secretase activity, and the accumulation of Abeta subsequently exacerbated TD-induced oxidative stress.Entity: Thiamine (thiamine), deficiency increases beta-secretase, TPP (Thiamine pyrophosphate), Alzheimer’s disease (AD), patients, amyloid precursor protein (beta-amyloid (Abeta)), neuroblastoma, BACE1 (beta-site APP cleaving enzyme 1), Abeta, ROS (reactive oxygen species), Abeta accumulation, mice. Question: predict the relationship between Disease entity "sclerosis" and Disease entity "multiple sclerosis", first choose from the following options: A. resembles B. no-relation C. others, please specify by generating a short predicate in 5 words. Answer: Let’s think step by step:
---|---
Output |  1\. Sclerosis is a disease of the central nervous system. 2. Multiple sclerosis is the most common form of sclerosis. 3. So the relationship between sclerosis and multiple sclerosis should be "sclerosis is a kind of multiple sclerosis". So the answer is: A. resembles. So the answer is: A. resembles
Table 8: An example to extract the relationship between “sclerosis” and “multiple sclerosis” using RE construction method. PubTator Type | Hetionet Type
---|---
Gene | genes
Chemical | compounds
Disease | diseases
Table 9: Entity type match from PubTator to Hetionet. Type-Type | Relations
---|---
genes-genes | ["covaries", "interacts", "regulates"]
disease-disease | ["resembles"]
compounds-compounds | ["resembles"]
genes-diseases | ["downregulates","associates","upregulates"]
genes-compounds | ["binds", "upregulates", "downregulates"]
compounds-diseases | ["treats", "palliates"]
Table 10: Type-type to relation match in Hetionet.

##  Appendix B Details of KG for LLMs

In this section, we provide detailed input and output for adopting KG to
augment LLMs, including path-based and neighbor-based sub-graph sampling
results (Table 11), self-aware knowledge retrieval (Table 12), describing sub-
graphs with LLMs (Table 13) and inference with sampled knowledge (Table 14).
The question we showcase here is “The area of the brain resistant to
Neurofibrillary tangles of Alzheimer’s disease is: A. Visual association areas
B. Entorhinal coex C. Temporal lobe D.Lateral geniculate body”, which same as
the one we use in Section 5.4.

Path-based Sub-graph |  | neurofibrillary tangles->FORM BY->microtubule-associated protein tau->BINDS->
---
(18)F-THK-5117->ADMINISTERED TO->rats->has->Alzheimer’s disease ->
Alzheimer’s disease ->affects->human->has->AD->DISEASE OF->Brain
entorhinal cortex->is a part of->brain->ASSOCIATES->
mouse with Alzheimer’s disease->brain region->temporal lobe
Neighbor-based Sub-graph |  | neurofibrillary tangles->FORM BY->microtubule-associated protein tau
---
Alzheimer’s disease ->causes->neuronal death
Alzheimer’s disease ->associates->cognitive decline
Alzheimer’s disease ->affects->human
Alzheimer’s disease ->has subtype->neurodegenerative diseases
Table 11: An example of path-based and neighbor-based sub-graph for the question. Input |  | There is a question and some knowledge graph. The knowledge graphs follow entity->relationship
---
->entity list format.
Graph:
neurofibrillary tangles->FORM BY->microtubule-associated protein tau->BINDS->
(18)F-THK-5117->ADMINISTERED TO->rats->has->Alzheimer’s disease ->Alzheimer’s
disease ->
affects->human->has->AD->DISEASE OF->Brain
entorhinal cortex->is a part of->brain->ASSOCIATES->
mouse with Alzheimer’s disease->brain region->temporal lobe}
Question:
The area of the brain resistant to Neurofibrillary tangles of Alzheimer’s
disease is:
A. Visual association areas
B. Entorhinal coex
C. Temporal lobe
D.Lateral geniculate body
Please rerank the knowledge graph and output at most 5 important and relevant
triples for
solving the given question. Output the reranked knowledge in the following
format:
Reranked Triple1: xxx ——>xxx
Reranked Triple2: xxx ——>xxx
Reranked Triple3: xxx ——>xxx
Reranked Triple4: xxx ——>xxx
Reranked Triple5: xxx ——>xxx
Answer:
Output |  | Reranked Triples1: entorhinal cortex ->is a part of ->brain
---
Reranked Triples2: entorhinal cortex ->associates ->mouse with Alzheimer’s
disease
Reranked Triples3: temporal lobe ->affected by ->Alzheimer’s disease
Table 12: An example of our self-aware knowledge retrieval in path-based sub-graph. Input |  | There are some knowledge graph paths. They follow entity->relationship->entity format.
---
Reranked Triples1: entorhinal cortex ->is a part of ->brain
Reranked Triples2: entorhinal cortex ->associates ->mouse with Alzheimer’s
disease
Reranked Triples3: temporal lobe ->affected by ->Alzheimer’s disease
Use the knowledge graph information. Try to convert them to natural language,
respectively.
Use single quotation marks for entity name and relation name.
And name them as Path-based Evidence 1, Path-based Evidence 2,…
Output:
Output |  | Path-based Evidence 1: ’Entorhinal cortex’ is a part of ’brain’.
---
Path-based Evidence 2: ’Entorhinal cortex’ associates ’mouse’ with
’Alzheimer’s disease’.
Path-based Evidence 3: ’Temporal lobe’ is affected by ’Alzheimer’s disease’.
Table 13: An example to describe the self-retrieved path-based sub-graph with the LLM. Input |  | Question: The area of the brain resistant to Neurofibrillary tangles of Alzheimer’s disease is:
---
A.Visual association areas
B.Entorhinal coex
C.Temporal lobe
D.Lateral geniculate body
You have some medical knowledge information in the following:
###Path-based Evidence 1: ’Entorhinal cortex’ is a part of ’brain’.
Path-based Evidence 2: ’Entorhinal cortex’ associates ’mouse’ with
’Alzheimer’s disease’.
Path-based Evidence 3: ’Temporal lobe’ is affected by ’Alzheimer’s disease’.
###Neighbor-based Evidence 1: ’neurofibrillary tangles’ are ’FORMED BY’ ’
microtubule-associated protein tau’.
Neighbor-based Evidence 2: ’Alzheimer’s disease’ ’CAUSES’ ’neuronal death’.
Neighbor-based Evidence 3: ’Alzheimer’s disease’ ’ASSOCIATES’ with ’cognitive
decline’.
Neighbor-based Evidence 4: ’Alzheimer’s disease’ ’AFFECTS’ ’human’.
Neighbor-based Evidence 5: ’Alzheimer’s disease’ ’HAS SUBTYPE’
’neurodegenerative diseases’."
Answer: Let’s think step by step:
Output |  | In Alzheimer’s disease, neurofibrillary tangles are composed of
---
abnormal aggregations of tau protein within neurons, leading to
neuronal dysfunction and eventual cell death. The lateral geniculate
body is a relay center in the thalamus that processes visual
information from the retina to the visual cortex. Since the lateral
geniculate body is not directly involved in memory and cognitive
function, it is less likely to be affected by neurofibrillary tangles in
Alzheimer’s disease compared to other brain regions such as the
entorhinal cortex and temporal lobe which play crucial roles in memory
and cognitive processing. Therefore, the area of the brain resistant to
neurofibrillary tangles of Alzheimer’s disease is the lateral geniculate
body (option D)
Table 14: An example of inference with selected knowledge

##  Appendix C ADQA Benchmark

Table 15 shows the detailed prompt of our self-sample method to construct ADQA
benchmark. Table 16 gives the statistics of our ADQA benchmark.

Input |  | Judge whether the question below is related to Alzheimer’s Disease. Please answer yes or no.
---
Question: Treatable causes of dementia are ___.
a).AD b).Hypothyroidism c).Multi-infarct dementia d).SDH e).Hydrocephalus
Is the question related to Alzheimer’s Disease? Answer:
Output | Yes
Table 15: An example from MedMCQA to self-sample AD-related QA sample with LLMs. Dataset | MedQA | MedMCQA | MMLU | QA4MRE | Total
---|---|---|---|---|---
Number | 152 | 210 | 49 | 35 | 446
Table 16: Statistics of our ADQA benchmark.

##  Appendix D Further Experiment for RAG

| MedQA | MedMCQA | NMMLU | QA4MRE | AVG
---|---|---|---|---|---
Almanac w/ 256 chunk size | 50.0 | 69.0 | 67.3 | 62.9 | 62.3
Almanac w/ top 10 docuemnt | 48.7 | 68.6 | 65.3 | 62.9 | 61.4
Almanac w/ CoT | 50.0 | 65.7 | 77.6 | 65.7 | 64.7
Clinfo.ai w/ 256 chunk size | 48.6 | 66.7 | 81.6 | 65.7 | 65.7
Clinfo.ai w/ top 5 docuemnt | 43.4 | 68.1 | 77.6 | 68.6 | 64.4
Clinfo.ai w/ CoT | 48.7 | 68.6 | 79.6 | 68.6 | 65.0
Table 17: Further experiment in RAG methods with different hyper-parameter
settings.


* [175] Sangdoo Yun, Dongyoon Han, Sanghyuk Chun, Seong Joon Oh, Youngjoon Yoo, and Junsuk Choe.  Cutmix: Regularization strategy to train strong classifiers with localizable features.  In ICCV, pages 6022–6031, 2019.
  * [176] Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna P. Gummadi.  Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment.  In WWW, pages 1171–1180. ACM, 2017.
  * [177] Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna P. Gummadi.  Fairness constraints: Mechanisms for fair classification.  In AISTATS, pages 962–970, 2017.
  * [178] Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell.  Mitigating unwanted biases with adversarial learning.  In AIES, pages 335–340, 2018.
  * [179] Hantian Zhang, Xu Chu, Abolfazl Asudeh, and Shamkant B. Navathe.  Omnifair: A declarative system for model-agnostic group fairness in machine learning.  In SIGMOD, pages 2076–2088, 2021.
  * [180] Hongjing Zhang and Ian Davidson.  Facct.  pages 138–148. ACM, 2021.
  * [181] Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz.  mixup: Beyond empirical risk minimization.  In ICLR, 2018.
  * [182] Junzhe Zhang and Elias Bareinboim.  Fairness in decision-making - the causal explanation formula.  In AAAI, 2018.
  * [183] Yi Zhang and Zachary G. Ives.  Finding related tables in data lakes for interactive data science.  In SIGMOD, pages 1951–1966, 2020.
  * [184] Zheguang Zhao, Lorenzo De Stefani, Emanuel Zgraggen, Carsten Binnig, Eli Upfal, and Tim Kraska.  Controlling false discoveries during interactive data exploration.  In SIGMOD, pages 527–540, 2017.
  * [185] Yan Zhou and Sally A. Goldman.  Democratic co-learning.  In IEEE ICTAI, pages 594–602, 2004.
  * [186] Zhi-Hua Zhou and Ming Li.  Tri-training: Exploiting unlabeled data using three classifiers.  IEEE TKDE, 17(11):1529–1541, November 2005.
  * [187] Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, and Tom Goldstein.  Transferable clean-label poisoning attacks on deep neural nets.  In ICML, pages 7614–7623, 2019.
  * [188] Xiaojin Zhu.  Semi-Supervised Learning Literature Survey.  Technical report, Computer Sciences, University of Wisconsin-Madison, 2005\.