Paper: LARGE LANGUAGE MODELS FOR BIOMEDICAL KNOWLEDGE GRAPH CONSTRUCTION (ICLR-2024)

Reviewer comment (Evaluation):
The contribution of this study is confusing. The authors claim that they evaluate the performance of various LLM for better KG construction. However, the metrics used evaluate QA performance.

Author response:
To evaluate the LLMs, we needed to review all EMR notes and extract all factors, treatments, and coexisting terms. The evaluation is based on precision and recall, with the ground truth for comparison being the entity values available in the notes. The precision and recall we calculate provide an evaluation of the whole framework, thus the KG, not only the QA system. With these metrics, we can compare the relations our framework gives as an output to the ones extracted by medical practitioners.  We had a total of 360,000 EMR notes. To avoid excessive time consumption, we chose to conduct the final analysis only on notes related to one disease (age-related macula degeneration), thereby annotating only age-related macula degeneration notes. In the results section, we provide precision and recall results for all LLMs, besides some of the decoder-only models, with which it was not possible to get structured output essential for the KG. Thus we give separate analyses for those decoder-only models. Afterward, we concentrated on qualitative analysis in the last part of the results section comparing the results with the relations extracted by the SemMedDB, which is a repository of semantic predictions extracted from the titles and abstracts of all PubMed citations.

Reviewer comment (Reproducibility):
There is neither scientific illustration about how to design the prompt nor reference from previous study to show the prompt design process.

Author response:
Prompt design, which is provided under section 4.4, where we show that we use a systematic and task-agnostic process to construct prompts as outlined in Jimenez Gutierrez et al. (2022).

Reviewer comment (Experiments):
The experimental design is not completely illustrated.

Author response:
We are providing an end-to-end ML framework to construct KGs from the EMR notes. It includes several steps as presented in the 4th section:
1. Preprocessing
2. Disease-specific notes identification
3. Prompt design, which is provided under section 4.4, where we show that we use a systematic and task-agnostic process to construct prompts as outlined in Jimenez Gutierrez et al. (2022). 
4. Question design, the questions are categorized into three types: treatment-related,
factor-related, and coexists with related questions.
5. Relation extraction
6. Postprocessing

Reviewer comment (Writing):
The KG construction process is not clearly articulated.

Author response:
You can find the KG construction process under the 4th section.

Reviewer comment (Reproducibility):
Could you share more information about LLM-based KG given the public dataset?

Author response:
The dataset we used is a real-world EMR notes provided by the Macula & Retina Institute. You can find some examples in the Appendix section. The decision to use this dataset was to ensure that none of the LLMs have been trained or fine-tuned on it, ensuring a fair comparison. At the end of the "Related Work" section, we describe why this dataset poses greater challenges compared to those in other papers. Thank you for the suggestion, we will consider using a public dataset for the evaluation of our method in future versions of our papers, but also before that we should ensure that none of the models have been trained/or fine-tuned on that.

Reviewer comment (Evaluation):
How to evaluate the quality of KG construction?

Author response:
You can find about the evaluation of the KG in the aforementioned text.

Reviewer comment (Evaluation):
Why use QA performance to demonstrate KG construction performance?

Author response:
The precision and recall we calculate provide an evaluation of the whole framework, thus the KG, not only the QA system. With these metrics, we can compare the relations our framework gives as an output to the ones extracted by medical practitioners.

Reviewer comment (Reproducibility):
How did you construct your KG? Just by using the prompts?

Author response:
We are providing an end-to-end ML framework to construct KGs from the EMR notes. It includes several steps as presented in the 4th section:
1. Preprocessing
2. Disease-specific notes identification
3. Prompt design, which is provided under section 4.4, where we show that we use a systematic and task-agnostic process to construct prompts as outlined in Jimenez Gutierrez et al. (2022). 
4. Question design, the questions are categorized into three types: treatment-related,
factor-related, and coexists with related questions.
5. Relation extraction
6. Postprocessing

Reviewer comment (Experiments):
Do prompts impact the model performance?

Author response:
Yes, prompts impact the KG construction process as can be visible in Table 2. The results are different by using 0-shot, few-shot, instruction-based, and our demonstrated "guided" prompt.

Reviewer comment (Experiments):
Authors take a preprocessing operation for clinic notes. So, how to ensure its reasonableness. It would be better to demonstrate the different performance of LLMs on processed data and raw data, respectively.

Author response:
1) We will add that comparison in the next version of our paper.

Reviewer comment (Evaluation):
This study is mainly related to an assessment work for different LLMs; however, authors only evaluate the precision and recall on three medical entities in Tab. 5 (I think it should be Tab.2) besides demonstrating some cases to prove their observations. It would be better to discuss different evaluations for different LLMs, such as reasons of hallucination, accuracy and evidence to extracted triplets, and so on.

Author response:
2) accuracy and evidence to extracted triplets: such analysis is provided in the "Qualitative Example: AMD" part of the Results section. We analyze each relation by bringing forward scientific literature proving their truthfulness. 
hallucination: usually it is hard to analyze the reason for the hallucinations of LLMs, it may be related to the domain shift between the training and testing data. We will consider finetuning models in such data and experimenting with them in future versions of our paper.  

1. The correlation between the performance assessment outlined in Table 2 and triple extraction results are following:

Each column in the table corresponds to a specific relation type, such as 'treatment.' Precision represented as 95%, and recall, represented as 90%, indicate that, within the specified method, the model successfully extracts 90% of triplets (head entity, relation = treatment, tail entity) from the available data, and 95% of these extracted triplets are accurate.  The ground truth for comparison is the triplets available in the notes. All the available triplets have been extracted by medical practitioners manually. 
Additionally, reconstructing the Knowledge Graph (KG) is facilitated by connecting head entities to tail entities using the corresponding relation edges.

This structured methodology allows for logical inferences on the KG. For example, if entity A treats entity B, and B is a factor for C, one can infer that A treats C as well. The "Qualitative Example: AMD" section presents all extracted triplets where the tail entity is AMD. Entities highlighted in red within the table signify inaccuracies in the triplets (head entity = red-colored entity, relation, tail entity = AMD), while an orange-colored triplet indicates the model's failure to extract a triplet present in the data (clinical notes). 

2. We have faced such things during the design of our proposed "guided" instruction-based prompting.  I agree with your opinion, we will add the evolution of the prompt from standard to our design and give an analysis of what may cause confusion of the LLM to hallucinate.

Reviewer comment (Experiments):
In Tab.2, there have no explicit results to show the effectiveness of guide instruction-based prompting. It would be better to show the performance of “Vicuna-33B w/o guide, Llama-2-70B w/o guide and WizardLM-70B w/o guide”.

Author response:
3) Quantitative results for decoder-only models (including Vicuna-33B, Llama-2-70B, and WizardLM-70B ) using 0-shot, few-shot, and instruction-based prompting techniques are not available. These models (with any of these three prompting techniques) did not produce structured outputs, rendering them unsuitable for our task. Guided instruction-based prompting has shown significant improvements for the 3 models provided in Table 2. So, there are no quantitative results without guided prompt design for those three models.

3. The success metric for us when evaluating "guided" prompt design was not to achieve better results than encoder-decoder results, but to make decoder-only models to give enough structured outputs which is essential for relation extraction. Our hypothesis is that with finetuning on health-related data and with our prompt design they may perform much better. But for now, they have at least started to give responses at some structured level, thus pushing the performance of some of the decoder-only models from 0's to the numbers available in Table 2. Your suggestion on ablation studies is definitely valuable and we will consider it adding to our next version of the paper.

Reviewer comment (Experiments):
Why do not evaluate GPT-4? Could authors carry out an experiment based on GPT-4?

Author response:
4) We are using a private dataset. We have not experimented with any of the GPTs for data privacy reasons. 

4. We have evaluated ChatGPT for the KG construction for AMD, it gave a very small number of relations compared to our results by using the clinical notes. We will add a comparison with ChatGPT to our paper.

Reviewer comment (Presentation):
The presentations should be improved. For example:  
(1) “Tab.5” in “Precision and Recall Results” should be “Tab.2”;  
(2) What does “Section ? 0.8” in “DATASET PREPROCESSING” refer to?  
(3) Why put the “hallucination” in Appendix?  
(4) What does the concepts of “Coexists with, Factor, Treatment” represent? “question types”, “medical entities”, or “relations”.

Author response:
5.1) Changed 
5.2) You can find clarification in the new version
5.3) All of the examples (including hallucinations) are quite long, so we have decided to put them in the Appendix
5.4) The explanations are presented in the 4.5 section. Here they are: 

a) The treatment-related questions inquire about methods to slow down the progression, decrease the chance, or reduce the risk of a specific condition. 
b) The factor-related questions aim to identify the causes, factors, or risks associated with a condition. 
c) The coexists with-related questions explore any symptoms, effects, diseases, clinical tests, or behaviors that may manifest in the patient while experiencing the disease

Reviewer comment (Presentation):
In sections 3.1 and 4.1, there is a non-existent reference to `"Section ?0.8"`.

Author response:
Addressed in the latest version.

Reviewer comment (Presentation):
The section cross-references are not in a unified format. Some examples include "Section A", "Appendix Appendix D", "4.1", "Appendix subsubsection E.2.1".

Author response:
Addressed in the latest version.

Reviewer comment (Presentation):
There is a "Table 4.2" in section 4.2. And "Table 5" in section 5 should actually refer to Table 2.

Author response:
Addressed in the latest version.

Reviewer comment (Writing):
The authors use past tense and present tense alternatively throughout the paper.

Author response:
Yes, we agree. We addressed all of these comments in the latest version.

Reviewer comment (Presentation):
In Algorithm 2, the symbol between "similarity_score" and "threshold" is not correctly shown.

Author response:
Addressed in the latest version.

Reviewer comment (Evaluation):
In section 4.1, the authors mention using the BioBERT NER model to extract a list of diseases. However, an evaluation of its accuracy is missing, and it is possible that the NER model could introduce erroneous information or ignore essential diseases.

Author response:
We will add the evaluation of BioBERT NER in the next version of our paper.

Reviewer comment (Experiments):
For the prompt designs, it would be better for the authors to discuss the reasons why they have such designs and why these designs could help.

Author response:
For instruction-based prompting, we follow a systematic and task-agnostic process to construct prompts as outlined in Jimenez Gutierrez et al. (2022). As depicted in the left examples in Figure 1, this method identifies three key components of a prompt: overall task instructions, a sentence introduction, and a retrieval message. Building upon this, we introduce guided instruction-based prompting, denoted as 'guided' in the forthcoming sections. This refined prompt structure incorporates three fundamental elements:

a) Contextual Task Instruction: This aspect furnishes explicit and comprehensive guidance, emphasizing the model's role in extracting information solely from the provided context. It establishes a clear framework for understanding the task at hand.

b) Input Format Guidance: To mitigate issues of relations extracted out of context, we introduced explicit instructions on how the model should interpret and process input. This includes specifying the acceptable types and formats for questions and contexts.

c) Output Format Guidance: We refined the retrieval message to include the entity type and explicitly instruct the model on the desired format for its responses.

Reviewer comment (Writing):
In section 4.3, the authors "explore two distinct approaches for aligning large language models to the task: open-book QA and in-context learning." According to the explanations in this section, "in the case of open-book QA, a query consists of a question and a context", however, it is also said that for in-context learning, "it extends the prompt to include both the question and the context." The two statements seem to be the same, and the difference between the two approaches should be clarified.

Author response:
Thank you. We have given a more detailed description in the paper. Here is the summary: 

a) Open-Book QA: In this approach, the context serves as a specific, external information source from which the model is expected to extract answers to the posed questions. The context is typically a passage, document, or a set of information separate from the model's pre-existing knowledge. The model's task is to directly reference and pull information from this provided context to respond to the query. Thus, the context in open-book QA acts as a discrete reference material that the model consults to find answers.

b) In-Context Learning: While in-context learning also involves a question and a context, the usage and purpose of the context differ significantly. Here, the context is not an external source of information but an integral part of the model's prompt. It includes a series of input-output pairs that serve as examples to guide the model in understanding the nature of the task. These examples demonstrate how to process and respond to similar queries. Therefore, the context in in-context learning is instructional and part of the learning material embedded within the prompt, guiding the model's response generation process based on demonstrated patterns, rather than serving as a source from which to extract direct answers.

In summary, while both approaches use a combination of questions and context, the role of the context in open-book QA is as an external reference source, whereas in in-context learning, it is an intrinsic part of the learning prompt, providing instructional examples rather than factual content for direct query response.

Reviewer comment (Novelty):
You fail to sufficiently contextualize your advancement in the significant body of related literature in this space. There are a number of papers that complete or otherwise generate knowledge graphs via LLMs, and you don't reference or compare to these at all.

Author response:
Thank you for the comments.

1) We will add them in the Related Work section.

Reviewer comment (Experiments):
The paper's section on results looks at a very specific task (age-related macula degeneration). Understanding how complex this dataset is compared to others would be really helpful.

Author response:
To evaluate the LLMs, we needed to review all EMR notes and extract all factors, treatments, and coexisting terms. The evaluation is based on precision and recall, with the ground truth for comparison being the entity values available in the notes. We had a total of 360,000 EMR notes. To avoid excessive time consumption, we chose to conduct the final analysis only on notes related to one disease (age-related macula degeneration), thereby annotating only age-related macula degeneration notes.

One of the main contributions of our paper, besides presenting an end-to-end method leveraging LLMs for the automatic construction of KGs, is an extensive evaluation of the performance of 12 LLMs of various architectures specifically tailored for clinical relation extraction.
The dataset we used is a real-world EMR notes provided by the Macula & Retina Institute. You can find some examples in the Appendix section. The decision to use this dataset was to ensure that none of the LLMs have been trained or fine-tuned on it, ensuring a fair comparison. At the end of the "Related Work" section, we describe why this dataset poses greater challenges compared to those in other papers.

Reviewer comment (Experiments):
MIMIC-II is publicly available, but there are datasets like i2b2 which are carefully annotated by physicians and are subject to availability based on a license. Have you considered using this for evaluation?

Author response:
Thank you for the suggestion; we will consider using i2b2 for the evaluation of our method in future versions of our papers.

Reviewer comment (Evaluation):
Can you explain why your method has recall 1 and precision 0.98 with FLAN-UL2?

Author response:
Our hypothesis is that prompt design and instructions specifically tailored for this use case aid LLMs in identifying all possible relations from the provided clinical notes.
We provide guided prompt design for utilizing LLMs in this task.