# MultifacetEval: Multifaceted Evaluation to Probe LLMs in
Mastering Medical Knowledge

Yuxuan Zhou  Xien Liu Corresponding author Chen Ning&Ji Wu Department of
Electronic Engineering, Tsinghua University, Beijing, 100084, China {zhou-
yx21<EMAIL_ADDRESS>{xeliu<EMAIL_ADDRESS>
###### Abstract

Large language models (LLMs) have excelled across domains, also delivering
notable performance on the medical evaluation benchmarks, such as MedQA.
However, there still exists a significant gap between the reported performance
and the practical effectiveness in real-world medical scenarios. In this
paper, we aim to explore the causes of this gap by employing a multifaceted
examination schema to systematically probe the actual mastery of medical
knowledge by current LLMs. Specifically, we develop a novel evaluation
framework MultifacetEval to examine the degree and coverage of LLMs in
encoding and mastering medical knowledge at multiple facets (comparison,
rectification, discrimination, and verification) concurrently. Based on the
MultifacetEval framework, we construct two multifaceted evaluation datasets:
MultiDiseK (by producing questions from a clinical disease knowledge base) and
MultiMedQA (by rephrasing each question from a medical benchmark MedQA into
multifaceted questions). The experimental results on these multifaceted
datasets demonstrate that the extent of current LLMs in mastering medical
knowledge is far below their performance on existing medical benchmarks,
suggesting that they lack depth, precision, and comprehensiveness in mastering
medical knowledge. Consequently, current LLMs are not yet ready for
application in real-world medical tasks. The codes and datasets are available
at https://github.com/THUMLP/MultifacetEval.

##  1 Introduction

The rapid advancement of large language model (LLM) technology has achieved
great success in various domains Romera-Paredes et al. (2023); Madani et al.
(2023); Boiko et al. (2023). Current LLMs encode extensive knowledge through
pretraining on massive unlabeled data. Some are further finetuned on
supervised datasets to be adapted to specific downstream tasks. Recently,
famous general LLMs like GPT-4 OpenAI (2023) and Gemini-pro Team et al.
(2023), as well as medical-domain-specific LLMs such as Med-PaLM Singhal et
al. (2023a), are reported to have encoded vast medical knowledge and achieved
significant performance on several medical benchmarks, surpassing previous
state-of-the-art models by a considerable margin Kung et al. (2023); Nori et
al. (2023a, b). Nevertheless, despite their impressive performance on existing
benchmarks, these LLMs still face challenges in addressing real-world medical
problems Thirunavukarasu et al. (2023); Clusmann et al. (2023); Wornow et al.
(2023). This leads to a significant gap between evaluation results and
practical performance in the medical domain. Therefore, in this paper, we aim
to study the underlying causes of this gap by systematically investigating the
depth of medical knowledge mastery in current LLMs.

Figure 1: GPT-3.5-turbo responding to medical exam problems assessing the same
knowledge point but in different formats.

Several medical benchmarks have been proposed to measure LLMs’ capacities in
the medical domain. Most current medical benchmarks assess LLMs by medical
question-answering tasks Jin et al. (2021); Pal et al. (2022); Hendrycks et
al. (2020); Jin et al. (2019); Ben Abacha et al. (2017, 2019); Singhal et al.
(2023a). Other benchmarks also evaluate LLMs in the forms of medical dialogue
Zeng et al. (2020) or other traditional NLP tasks (e.g., relation extraction,
NER) based on medical corpora Zhang et al. (2022). Nevertheless, most existing
medical benchmarks rely on a specific question type (e.g., multiple-choice
questions) to evaluate LLMs. Therefore, they may overestimate the performance
of current LLMs, as certain LLMs may have been finetuned for specific question
types. Consequently, their performance on specific question types would
significantly surpass those on other questions. Even if some benchmarks
evaluate LLMs’ medical capabilities from various facets, each facet is
evaluated based on distinct sets of knowledge points. Therefore, the outcomes
of these benchmarks still cannot reflect LLMs’ mastery on the same knowledge
points across diverse facets. Meanwhile, we found it necessary to conduct
multifaceted evaluation on the same knowledge point. Figure 1 illustrates
GPT-3.5-turbo’s response to two medical exam problems assessing the same
knowledge point but in different question types. The multiple-choice question
is extracted from the United States Medical Licensing Examination (USMLE). In
contrast, the true-false question is adapted from the original question by
substituting the phrase “Which of the following” with the correct option,
evaluating LLMs’ ability to verify statements based on corresponding medical
knowledge. Although GPT-3.5-turbo successfully chooses the symptom of the
patient’s disease, it judges the statement that is consistent with MCQ’s
answer as false, conflicting with its previous prediction. This highlights the
importance of conducting multifaceted evaluations on identical medical
knowledge points for a systematically analysis of LLMs’ knowledge mastery.

Figure 2: Principle of the proposed multifaceted evaluation.

In contrast to existing evaluation benchmarks, current education systems
generally utilize various assessment methods, including assignments, quizzes,
projects, and exams, to evaluate students’ comprehensive mastery of the same
knowledge point from multiple facets. Inspired by this, we propose a novel
multifaceted evaluation approach MultifacetEval to evaluate the actual medical
knowledge mastery of current LLMs from multiple facets. Figure 2 illustrates
the principle of this approach. Specifically, we generate a series of
questions for each knowledge point of interest with various question types.
These questions emphasize evaluating this knowledge point from different
facets, including comparison, discrimination, verification, and rectification
capabilities. Therefore, the proposed approach would provide a more
comprehensive evaluation of LLMs’ medical knowledge mastery compared with
conventional medical benchmarks that rely on a single evaluation facet. The
proposed approach also possesses strong versatility, as it can generate
multifaceted questions by directly crafting them based on knowledge points in
medical knowledge bases or rephrasing questions on existing medical evaluation
benchmarks.

To validate the effectiveness of the proposed multifaceted evaluation method,
we apply the proposed method to construct two new evaluation datasets based on
a medical knowledge database and a medical benchmark MedQA Jin et al. (2021),
respectively. A total of 13 well-known general and medical LLMs are evaluated
on these datasets. The experimental results indicate that current LLMs lack a
comprehensive, precise, and in-depth mastery of medical knowledge despite
their considerable performance on existing medical benchmarks. Moreover, the
results demonstrate that current LLMs possess excellent comparison capability,
while they have not well mastered other capabilities such as discrimination,
verification, and rectification in the medical domain. Our contributions can
be summarized as follows:

  * •

We propose a novel multifaceted evaluation schema (MultifacetEval) to evaluate
LLMs’ medical knowledge mastery on the same knowledge point from various
facets instead of a single facet in existing benchmarks. The proposed method
can more accurately evaluate the LLMs’ mastery of medical knowledge.

  * •

Based on the proposed method, we generate two novel multifaceted datasets,
MultiDiseK and MultiMedQA, based on a medical knowledge base and a well-known
medical benchmark MedQA, respectively. The performance of these two datasets
can more comprehensively reflect LLMs’ mastery of medical knowledge.

  * •

The experimental results reveal that the genuine extent of medical knowledge
mastery in current LLMs is significantly lower than that evaluated by existing
medical benchmarks. Additionally, we observe substantial variations in LLMs’
performance across different facets.

Figure 3: Framework of the proposed multifaceted evaluation approach that
evaluates LLMs’ medical knowledge mastery from various facets.

##  2 Related Work

#### Large Language Models on Medical Tasks

Recently, some famous LLMs are reported to encode medical knowledge and
achieve considerable performance on existing medical benchmarks. General LLMs
such as Flan-PaLM and GPT-4 are reported to achieve state-of-the-art
performance on multiple datasets Singhal et al. (2023a); Nori et al. (2023b).
For example, they achieve accuracies of 67.6 and 90.2 on a medical exam
benchmark MedQA Jin et al. (2021), largely surpassing the prior SOTA models.
Several LLMs specially pretrained or finetuned on the medical corpora, such as
Med-PaLM, Med-PaLM2 Singhal et al. (2023b), ClinicalCamel Toma et al. (2023),
and Med42 Christophe et al. (2023), are also proposed to address problems in
the medical domain and achieve high performance on various medical benchmarks.
However, these models cannot tackle problems in real medical scenarios. Our
study aims to investigate the gap between the high evaluation performance and
the limited practical effectiveness of existing LLMs in the medical domain.

#### Medical Evaluation Benchmarks

Current medical evaluation benchmarks can be classified into three classes:
(1) Question-answering datasets with problems collected from different
sources, including medical exams Jin et al. (2021); Pal et al. (2022);
Hendrycks et al. (2020), scientific literature Jin et al. (2019), and consumer
health questions Ben Abacha et al. (2017, 2019); Singhal et al. (2023a); (2)
medical dialogue datasets Zeng et al. (2020); Yang et al. (2020); (3) datasets
Peng et al. (2019); Zhang et al. (2022) involving conventional NLP tasks (NER,
relation extraction, NLI) on medical corpora. Some of these datasets assess
LLMs from a single facet. Others evaluate LLMs with multiple tasks, while the
tasks are constructed on different groups of knowledge points. In this paper,
we design a new evaluation method to evaluate LLMs’ mastery of the same
knowledge point from multiple facets.

##  3 Knowledge-Centric Multifaceted Evaluation

###  3.1 Multifaceted Evaluation Schema

The proposed multifaceted evaluation approach is motivated by the current
education systems, where various assessment methods, including assignments,
projects, and exams, are employed to comprehensively evaluate whether students
have truly mastered a particular knowledge point. Given a knowledge point
$\mathrm{k}$ and a series of $N$ evaluation facets
$\mathbf{f}=[f^{1},f^{2},\cdots,f^{N}]^{\textbf{T}}$, the performance of an
LLM ($\mathrm{M}$) evaluated through the proposed multifaceted evaluation is:

$\mathbf{f}_{\mathrm{k}}(\mathrm{M})=[f^{1}_{\mathrm{k}}(\mathrm{M}),f^{2}_{\mathrm{k}}(\mathrm{M}),\cdots,f^{N}_{\mathrm{k}}(\mathrm{M})]^{\textbf{T}}$
(1)

Where $f^{i}_{\mathrm{k}}$ denotes the specific questions designed to
emphasize the evaluation of the $i^{th}$ facet related to the knowledge point
$\mathrm{k}$, and $f^{i}_{\mathrm{k}}(\mathrm{M})\in\\{0,1\\}$ is the
evaluation outcome: $f^{i}_{\mathrm{k}}(\mathrm{M})=1$ if all questions in
$f^{i}_{\mathrm{k}}$ are answered correctly, and 0 otherwise. Compared with
single-faceted evaluation, the proposed multifaceted evaluation method conveys
more comprehensive information about the mastery of a specific knowledge
point. The proposed multifaceted evaluation schema demonstrates strong
transferability: it can be applied in other domains by adjusting evaluation
facets and question generation strategies.

###  3.2 Facets of Medical Knowledge Mastery

We employ the proposed evaluation schema to systematically probe current LLMs
in mastering medical knowledge. Considering the characteristics of medical
scenarios, we set $N=4$ and design a total of four evaluation facets of
capabilities that are essential for solving real medical problems:
Comparison ($f^{1}$): The ability to compare different medical entities/events
and choose the most suitable one that meets some criteria. It is crucial for
medical applications such as diagnosis and drug recommendation.
Rectification ($f^{2}$): The capability to identify errors in the medical
process (treatment, diagnosis) and offer corresponding corrections.
Rectification plays an important role in medical scenarios such as computer
aided diagnosis.
Discrimination ($f^{3}$): The capacity to recognize and differentiate between
medical concepts accurately. Discrimination of medical concepts is the bedrock
of medical applications such as clinical decision support and personalized
medicine.
Verification ($f^{4}$): The ability to determine the veracity of a statement
based on the acquired knowledge. Such capability is highly demanded in the
quality assessment of electronic health records and laboratory results.

###  3.3 Multifaceted Medical Evaluation Framework

Built on the facets discussed above, we design a multifaceted medical
evaluation framework to comprehensively evaluate mastery of medical knowledge
by LLMs from these evaluation facets. Figure 3 illustrates an overview of the
proposed multifaceted evaluation framework. Given a set of medical knowledge
points $\mathcal{K}$, the framework evaluates LLMs’ mastery of medical
knowledge through two steps:

#### Multifaceted Question Generation

In the first step, we generate multiple questions from diverse evaluation
facets for each knowledge point in the set:
$\mathrm{k}\to\\{f^{i}_{\mathrm{k}}|1\leq i\leq N\\}$, $\text{where
}\mathrm{k}\in\mathcal{K}$. Specifically, we design four question types,
including multiple-choice questions, revision questions, multiple-answer
questions, and true-false questions, to emphasize the evaluation of the
comparison, rectification, discrimination, and verification facets,
respectively:
(1) Multiple-Choice Questions: We maintain the multiple-choice questions (MCQ)
applied in existing benchmarks to emphasize the evaluation of the comparison
facet. A multiple-choice question comprises a question and multiple options
(typically 4). To answer multiple-choice questions accurately, participants
must compare the given options and select the most suitable choice that fits
the question.

Figure 4: Example of the proposed revision question.

(2) Revision Questions: We design a new question type named “revision”
question (RQ) to focus on evaluating the rectification capabilities of LLMs.
Figure 4 illustrates an example of this question type. A revision question
comprises a multiple-choice question and a provided option (not necessarily
correct) to this question. Participants are asked to recheck the correctness
of the given option based on the question, and revise the answer appropriately
if needed.
(3) Multiple-Answer Questions: We consider leveraging multiple-answer
questions (MAQ) to highlight the evaluation of the discrimination capability.
In contrast to MCQs, a multiple-answer question consists of several options,
with one or more aligning with the given question. Effectively answering MAQs
requires a comprehensive and precise mastery of discriminative knowledge for
all options, as MAQ answers cannot be determined through option-wise
comparison.
(4)True-false Questions: We utilize true-false questions (TFQ) to emphasize
the assessment of the verification facet. A true-false question generally
presents a statement that can be verified based on the corresponding medical
knowledge and information provided in the statement. True-false questions do
not include options that may provide clues, requiring ones to mastery medical
knowledge accurately.

#### Multifaceted LLM Evaluation

In the next step, we evaluate the LLM $\mathrm{M}$ with the generated
multifaceted questions to obtain comprehensive evaluation results on each
knowledge points: $f^{i}_{\mathrm{k}}\to f^{i}_{\mathrm{k}}(\mathrm{M})$
$\text{where }k\in\mathcal{K}\text{ and }1\leq i\leq N$. Finally, the proposed
evaluation framework produces comprehensive evaluation outcomes for
$\mathrm{M}$ across all the knowledge points:
$\\{\mathbf{f}_{\mathrm{k}}(\mathrm{M})|\mathrm{k}\in\mathcal{K}\\}$. To
reflect the LLM’s comprehensive mastery of individual knowledge points, we
define a knowledge point $\mathrm{k}$ is mastered by $\mathrm{M}$ under facets
$\\{f^{1},f^{2}...,f^{N}\\}$, if the function

$r_{\mathrm{k}}(\mathrm{M})=\prod_{i=1}^{N}f^{i}_{\mathrm{k}}(\mathrm{M})$ (2)

equals 1. Here, $r_{\mathrm{k}}(\mathrm{M})=1$ only when
$f^{i}_{\mathrm{k}}(\mathrm{M})=1$ holds for $1\leq i\leq N$, indicating
accurate answers to all questions from these facets. The overall performance
can therefore be represented as the proportion of mastered knowledge points:

$p(\mathrm{M})=\frac{1}{|\mathcal{K}|}\sum_{\mathrm{k}\in\mathcal{K}}r_{\mathrm{k}}(\mathrm{M})$
(3)

##  4 Experiments

Figure 5: Aspects of disease-related knowledge in DiseK.

###  4.1 Experiment Setup

Figure 6: Proportion of mastered knowledge points ($p(\mathrm{M})$) evaluated
by single-faceted and multi-faceted methods on two datasets. Dash dotted lines
refer to LLMs with sizes under 70B, while solid lines denote LLMs larger than
70B. Evaluated facets are added following the sequence: comparison,
verification, rectification, and discrimination.

#### MultiDiseK Dataset Generation

Medical knowledge bases explicitly contain knowledge points that can be
directly utilized in the proposed method. In this paper, we introduce a
disease-centric knowledge base (DiseK) and construct a multifaceted evaluation
dataset (MultiDiseK) from it. DiseK is annotated by 20 medical experts for
about 3 months. It consists of 1,000 common diseases, accompanied by 4
fundamental aspects of medical knowledge (illustrated in Figure 5). These
aspects are closely associated to the clinical decision-making process,
involving diagnosis and treatment. Therefore, LLMs must acquire these aspects
of medical knowledge to be applicable in clinical decision support systems
(CDSS) Wu et al. (2018); Liang et al. (2019).

The MultiDiseK dataset is constructed based on medical knowledge points in
DiseK by using carefully crafted question templates. For the comparison facet,
an MCQ (4-option) is generated for each aspect of disease knowledge, where
options are formed by selecting attributes that either belong or do not belong
to the specified disease. Revision questions are generated by rephrasing each
MCQ into two questions, providing either the correct choice or a randomly
selected incorrect choice. Multiple-answer questions are generated similarly
to MCQs, but with selecting 1-3 attributes as correct options. For true-false
questions, we randomly choose an attribute with 50% probability associated
with the disease and 50% not associated. Participants are then asked to
determine whether the given attribute is associated with the disease. For all
questions crafted above, we also generate a corresponding negated version by
incorporating negation words into the question and modifying the answers
correspondingly. This is done to further assess the depth of knowledge mastery
by LLMs. Finally, the constructed dataset encompasses a total of 3,167
disease-related knowledge points (some diseases may not have corresponding
medications or surgeries), including 6,334 MCQs, 12,668 RQs, 6,334 MAQs, and
6,334 TFQs. More details of this dataset (e.g., question templates, dataset
statistics) are presented in Appendix A and B.

#### MultiMedQA Dataset Generation

To make our proposed multifaceted evaluation approach comparable with existing
benchmarks, we further construct another dataset MultiMedQA based on a medical
benchmark MedQA Jin et al. (2021), since several LLMs have achieved notable
performance on this benchmark. MedQA is a medical exam dataset that contains
5-option multiple-choice questions from the professional medical board exams
of different sources. The question in MedQA typically consists of a patient’s
medical consultation record followed by a question related to the patient’s
situation (e.g., diagnosis, the next step in management, findings of
diagnostic tests). Employing the multifaceted evaluation schema, we rephrase
each MedQA question into various types, conducting a multifaceted evaluation
of the medical knowledge points embedded in MedQA. To do so, we first selected
800 questions suitable for the multifaceted adaptation from the US exam part
(1,273 questions) by regular expressions. After that, we rephrase them into
multifaceted questions with heuristic rules. For revision-type questions, we
generate them using a method similar to that applied in MultiDiseK
construction. For multiple-answer questions, given the challenge of explicitly
identifying the knowledge points in the original question, we adopt a solution
by retrieving synonyms for the correct option and randomly replacing 0-3
incorrect options with these synonyms to generate a new question. Each
question is further paired with a negated version by introducing a negation
word in the question. True-false questions were generated by substituting the
interrogative word/phrase (e.g., “which of the following”) with either the
correct or incorrect option selected from the remaining four options, and
negated versions were also created using the same method. The resulting
MultiMedQA dataset includes 800 MCQs, 1,600 RQs, 1,600 MAQs, and 3,200 TFQs.
More details of MultiMedQA are provided in Appendix C.

#### Evaluation Setting

We evaluate LLMs by five-shot learning on the proposed datasets. We report the
performance of LLMs under two settings: (1) answer-only Brown et al. (2020):
prompting LLMs with only question-answer pairs; (2) Chain-of-Thought with
Self-consistency (CoT+SC) Wang et al. (2022): prompting LLMs multiple times
with question-answer pairs and the chain-of-thoughts, aggregating the results
by majority vote to obtain the final answer. For the latter setting, we
generate CoTs following the method proposed in Nori et al. (2023b) and ask
LLMs each question 5 times in our implementation. We only apply the answer-
only setting for experiments in MultiDiseK since questions in MultiDiseK do
not require sophisticated reasoning in medical cases. We use carefully
designed regular expressions to extract answers and observe that they can
retrieve answers successfully in most cases. More details are provided in
Appendix D.

Model | Comp. | Rect. | Disc. | Veri. | Average
---|---|---|---|---|---
Random | 20.0 | 20.0 | 3.2 | 50.0 | 23.3
ChatGLM-6B | 27.7 | 20.3 | 5.7 | 50.6 | 26.1
Vicuna-7B | 21.0 | 17.7 | 2.1 | 49.4 | 22.6
Llama2-7B | 20.8 | 23.0 | 0.1 | 49.6 | 23.4
Bloomz-7.1B-mt | 25.4 | 11.9 | 5.5 | 50.1 | 23.2
Meditron-7B | 20.6 | 18.8 | 0.0 | 48.9 | 22.1
Pulse-7B | 19.9 | 14.9 | 0.7 | 49.2 | 21.2
Vicuna-13B | 20.1 | 17.4 | 0.6 | 51.7 | 22.4
Llama2-70B | 41.8 | 30.7 | 10.8 | 54.7 | 34.5
Meditron-70B | 47.2 | 28.3 | 5.1 | 50.8 | 32.8
ClinicalCamel-70B | 23.9 | 24.9 | 6.4 | 50.8 | 26.5
Med42-70B | 59.0 | 44.8 | 26.2 | 57.5 | 46.9
Gemini-pro | 41.0 | 37.2 | 12.5 | 59.2 | 37.5
GPT-3.5-turbo | 45.5 | 48.6 | 12.5 | 58.1 | 41.2
(a) Results in the setting of Answer-only.

Model | Comp. | Rect. | Disc. | Veri. | Average
---|---|---|---|---|---
Random | 20.0 | 20.0 | 3.2 | 50.0 | 23.3
ChatGLM-6B | 26.0 | 17.2 | 6.8 | 49.1 | 24.8
Vicuna-7B | 36.7 | 10.8 | 6.2 | 53.0 | 26.7
Llama2-7B | 35.1 | 18.1 | 5.6 | 51.2 | 27.5
Bloomz-7.1B-mt | 21.8 | 12.5 | 5.5 | 50.9 | 22.7
Meditron-7B | 29.1 | 13.3 | 4.8 | 50.2 | 24.3
Pulse-7B | 28.9 | 19.6 | 5.3 | 50.5 | 26.1
Vicuna-13B | 29.7 | 15.8 | 5.7 | 52.1 | 25.8
Llama2-70B | 50.6 | 35.4 | 10.1 | 58.1 | 38.5
Meditron-70B | 53.1 | 33.9 | 10.8 | 56.4 | 38.5
ClinicalCamel-70B | 58.2 | 37.5 | 12.0 | 61.3 | 42.2
Med42-70B | 56.9 | 34.3 | 22.6 | 59.1 | 43.2
Gemini-pro | 59.4 | 40.2 | 34.5 | 64.2 | 49.6
GPT-3.5-turbo | 60.4 | 50.1 | 22.4 | 62.2 | 48.8
(b) Results in the setting of Chain-of-Thought Self Consistency.

Table 1: Five-shot accuracies on the MultiMedQA dataset across comparison (Comp.), rectification (Rect.), discrimination (Disc.), and verification (Veri.) capabilities. “Average” column denotes the macro average of accuracies across all facets. Model | Comp. | Rect. | Disc. | Veri. | Average
---|---|---|---|---|---
Random | 25.0 | 25.0 | 6.7 | 50.0 | 26.7
ChatGLM-6B | 35.2 | 27.7 | 18.5 | 52.8 | 33.5
Vicuna-7B | 29.5 | 24.9 | 14.0 | 55.1 | 30.9
Llama2-7B | 27.8 | 25.5 | 15.7 | 55.2 | 31.0
Bloomz-7.1B-mt | 34.0 | 21.0 | 17.6 | 53.3 | 31.5
Meditron-7B | 27.4 | 25.2 | 12.5 | 50.1 | 28.8
Pulse-7B | 26.1 | 22.2 | 2.8 | 52.9 | 26.0
Vicuna-13B | 25.6 | 25.6 | 9.2 | 52.5 | 28.2
Llama2-70B | 65.6 | 47.5 | 33.7 | 58.1 | 51.2
Meditron-70B | 66.3 | 50.3 | 38.7 | 63.1 | 54.6
ClinicalCamel-70B | 66.8 | 63.1 | 37.0 | 68.8 | 58.9
Med42-70B | 72.5 | 57.3 | 37.1 | 64.4 | 57.8
Gemini-pro | 81.7 | 72.6 | 55.0 | 77.0 | 71.6
GPT-3.5-turbo | 74.1 | 59.1 | 44.8 | 63.7 | 60.4
Table 2: Five-shot accuracies on the MultiDiseK dataset.

#### Metrics

We employ proportion of mastered knowledge points ($p(\mathrm{M})$ in Sec.3.3)
to measure the overall performance. For each facet, we employ accuracy
($\frac{\text{\\#correctly answered questions}}{\text{\\#all of the
questions}}$) as the fine-grained metric. For MAQs, correct predictions
require an exact match with the ground truth answers. Regarding RQs,
correctness is determined when both the veracity of the chosen option and the
original question’s answer are accurately predicted. We observe that some LLMs
“fortunately” achieve high accuracies on RQs by always staying consistent with
the provided option since half of the RQs provide the correct options.
Therefore, we revise the calculation of revision questions’ performance to
reduce this bias:
$\operatorname{acc}=\frac{1}{N_{o}}\operatorname{acc}_{T}+\frac{N_{o}-1}{N_{o}}\operatorname{acc}_{F}$,
where $N_{o}$ is the number of options, $\operatorname{acc}_{T}$ and
$\operatorname{acc}_{F}$ are the accuracies of RQs that provide correct and
incorrect options, respectively. The accuracy calculated above is proven to
reduce the impact of this bias, and we provide the corresponding proof in
Appendix E.

#### Baseline Models

We evaluate a total of 13 LLMs with varying sizes in this paper: (1) general
LLMs: ChatGLM (6B) Du et al. (2022), Llama2 (7B,70B) Touvron et al. (2023),
Vicuna (7B,13B) Zheng et al. (2023), Bloomz-mt (7.1B) Muennighoff et al.
(2023), GPT-3.5-turbo Ouyang et al. (2022) and Gemini-pro Team et al. (2023);
(2) medical LLMs: Pulse (7B) Xiaofan Zhang (2023), Meditron (7B,70B) Chen et
al. (2023), ClinicalCamel (70B) Toma et al. (2023), and Med42 (70B) Christophe
et al. (2023). We have not evaluated GPT-4 OpenAI (2023) and MedPaLM Singhal
et al. (2023a), since GPT-4 is too expensive and MedPaLM is not publicly
available yet.

###  4.2 Results

(a) Results on the MultiMedQA dataset.

(b) Results on the MultiDiseK dataset.

Figure 7: Multifaceted performance of LLMs across the evaluated facets on
MultiMedQA and MultiDiseK. Hatched bars: random guessing performance. Solid
bars above the hatched part: LLMs gain over random guessing. Meditron,
ClinicalCamel, and Med42 are all 70B versions.

#### Single-faceted vs. Multi-faceted

We first compare the proposed multifaceted evaluation with the conventional
single-faceted evaluation. Figure 6 illustrates the proportion of mastered
knowledge points ($p(\mathrm{M})$) by LLMs on the proposed MultiMedQA and
MultiDiseK datasets, evaluated using both single-faceted (comparison-type) and
multifaceted methods111Since current LLMs struggle to correctly answer both an
affirmative question and its negation simultaneously, we remove negated
questions in this analysis to ensure the visibility.. These LLMs are reported
to achieve high performance on existing medical benchmarks, including MedQA.
We report the performance on the MultiMedQA achieved by the CoT+SC setting
since these LLMs generally achieve higher performance in this setting. The
experimental results indicate that all LLMs above 70B have effectively
mastered a considerable number of knowledge points when evaluated solely from
the comparison facet (i.e., the original MedQA questions), consistent with
their reported performance on existing benchmarks. However, we observe a sharp
decline in the proportion of mastered knowledge points across various LLMs as
the number of evaluated facets increases. For example, GPT-3.5-turbo’s
performance evaluated by 4 facets is around 50% lower on MultiMedQA and 40%
lower on MultiDiseK compared with the single-faceted results. Moreover, we
observe that though several smaller LLMs (dash dotted lines) also perform
notably under single-faceted evaluation, their performance nearly approaches
zero when evaluated by $\geq 3$ facets. In contrast, larger LLMs master more
knowledge under multifaceted evaluation. We also study different sequences of
adding evaluation facets in Appendix F and observe that the conclusions remain
consistent. The results imply that current LLMs lack a comprehensive mastery
of medical knowledge.

#### Comparison Across LLMs

Table 1 and 2 compare LLMs performance across various datasets and settings.
LLMs generally perform better on the MultiDiseK dataset since the questions do
not involve analysis of specific medical cases. Gemini-pro achieves the
highest performance on both datasets with 49.6 and 71.6 in average accuracy,
respectively. GPT-3.5-turbo performs similarly to Gemini-pro on MultiMedQA
(48.8) but significantly lags behind Gemini-pro on MultiDiseK (60.4). The
discrepancy may be attributed to the broader coverage of disease knowledge by
Gemini-pro compared with GPT-3.5-turbo, while its ability to apply medical
knowledge in specific medical cases is similar to GPT-3.5-turbo. For open-
source LLMs in 70B size, we find that several medical LLMs (Med42,
ClinicalCamel) significantly surpass their base model LLama2-70B and achieve
comparable performance compared to GPT-3.5-turbo on both datasets (46.9 for
Med42 on MultiMedQA and 58.9 for ClinicalCamel on MultiDiseK). LLMs that are
not larger than 13B perform only slightly better than random guessing.
However, they achieve significantly higher performance in the comparison facet
and perform similarly or even worse than random guessing on facets such as
verification and rectification. One possible explanation is that these two
facets represent higher-level capabilities that can manifest only in LLMs with
larger sizes. Notably, the comparison-type questions in MultiMedQA are
directly sourced from the MedQA dataset. In our study, the performance of
GPT-3.5-turbo on this facet (60.4) aligns closely with the reported
performance in Nori et al. (2023b) (60.2), which could indicate the
reliability of our findings. Comparing the Answer-only setting with the CoT+SC
setting, we find that larger models significantly benefit more from CoT+SC
(except Med42). The effect of CoT+SC varies across facets: for Gemini-pro,
CoT+SC largely improves its performance in the comparison (+18.4) and
discrimination (+22.0) facets, while it has a limited effect on verification
(+5.0) and rectification facets (+3.0).

#### Comparison Across Multiple Facets

We further compare the top-5 LLMs’ performance across different capabilities
facets in Figure 7. The performance on the MultiMedQA is reported under the
CoT+SC setting as well. Note that the hatched bars represent the random
guessing performance of the corresponding question type. The experimental
results demonstrate that the evaluated LLMs typically exhibit the most
significant improvement over random guessing in the comparison facet, followed
by the rectification and discrimination facets, and lastly, the verification
facet. The high performance on the comparison facet may be caused by the fact
that current LLMs have seen more comparison-type questions (MCQs) in their
training data to perform well on existing benchmarks. Rectification-type
questions are more challenging than comparison-type questions because they
require LLMs to determine the correctness of the provided answer and to revise
it accurately. Discrimination-type questions also perform worse than
comparison-type questions, probably because of their demand for LLMs to
discern nuances between concepts instead of merely selecting the most suitable
choice. Verification-type questions exhibit the lowest gain, likely due to the
need for direct verification based on medical knowledge without additional
information from options.

##  5 Conclusion and Discussion

In this paper, we propose a multifaceted evaluation approach, MultifacetEval,
designed to probe the actual mastery of medical knowledge by current LLMs.
Following this methodology, we construct two multifaceted evaluation datasets,
MultiDiseK and MultiMedQA. The experimental results demonstrate that current
LLMs’ medical knowledge mastery is significantly lower than their performance
on medical benchmarks suggests, indicating that the proposed MultifacetEval
framework offers a more comprehensive assessment of LLMs’ medical knowledge
mastery. Furthermore, LLMs demonstrate significant variations in performance
across different evaluation facets. These results suggest that Current LLMs
generally lack a deep, precise, and comprehensive mastery of medical
knowledge, which is the probable cause of the disparity between high
performance on medical benchmarks and insufficient performance on real medical
scenarios. Moreover, although some smaller LLMs are reported to achieve
performance comparable to larger LLMs on several benchmarks, they achieve much
lower performance on multifaceted datasets, indicating that their mastery of
medical knowledge is not as comprehensive as that of larger LLMs.

The above conclusion also provides insights into the development of medical
foundation models: (1) Medical foundation models need to be sufficiently large
to master medical knowledge comprehensively, deeply, and precisely; (2) Their
training should cover a diverse range of medical tasks rather than being
restricted to specific ones, making them truly applicable in real-world
scenarios.

Finally, it is worth noting that our study is only a first step in exploring
the actual mastery of medical knowledge by LLMs. In the future, we plan to
evaluate LLMs across additional facets relevant to real medical applications
and expand the scale of knowledge points for evaluation, continuously
enhancing the comprehensiveness, professionalism, and robustness of the
proposed method.

## Acknowledgments

The work is supported by National Key R&D Program of China (2021ZD0113402). We
thank the anonymous reviewers for helpful comments and feedback.

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