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In A. Martin, K. Hinkelmann, H.-G. Fill, A. Gerber, D. Lenat, R. Stolle, F.
van Harmelen (Eds.), Proceedings of the AAAI 2023 Spring Symposium on
Challenges Requiring the Combination of Machine Learning and Knowledge
Engineering (AAAI-MAKE 2023), Hyatt Regency, San Francisco Airport,
California, USA, March 27-29, 2023.

[orcid=<EMAIL_ADDRESS>url=https://labs.engineering.asu.edu/labv2/, ]
[1] [orcid=<EMAIL_ADDRESS>url=, ]

[orcid=<EMAIL_ADDRESS>url=, ]

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[1]Corresponding author.

# An Independent Evaluation of ChatGPT on Mathematical Word Problems (MWP)

Paulo Shakarian  Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281,
USA  Abhinav Koyyalamudi  Noel Ngu  Lakshmivihari Mareedu

(2023)

###### Abstract

We study the performance of a commercially available large language model
(LLM) known as ChatGPT on math word problems (MWPs) from the dataset DRAW-1K.
To our knowledge, this is the first independent evaluation of ChatGPT. We
found that ChatGPT’s performance changes dramatically based on the requirement
to show its work, failing $20\%$ of the time when it provides work compared
with $84\%$ when it does not. Further several factors about MWPs relating to
the number of unknowns and number of operations that lead to a higher
probability of failure when compared with the prior, specifically noting
(across all experiments) that the probability of failure increases linearly
with the number of addition and subtraction operations. We also have released
the dataset of ChatGPT’s responses to the MWPs to support further work on the
characterization of LLM performance and present baseline machine learning
models to predict if ChatGPT can correctly answer an MWP. We have released a
dataset comprised of ChatGPT’s responses to support further research in this
area.

###### keywords:

Large Language Models Math Word Problems ChatGPT

##  1 Introduction

The emergence of large language models (LLM) has gained much popularity in
recent years. At the time of this writing, some consider OpenAI’s GPT 3.5
series models as the state-of-the art [1]. In particular, a variant tuned for
natural dialogue known as ChatGPT [2], released in November 2022 by OpenAI,
has gathered much popular interest, gaining over one million users in a single
week [3]. However, in terms of accuracy, LLMs are known to have performance
issues, specifically when reasoning tasks are involved [1, 4]. This issue,
combined with the ubiquity of such models has led to work on prompt generation
and other aspects of the input [5, 6]. Other areas of machine learning, such
as meta-learning [7, 8] and introspection [9, 10] attempt to predict when a
model will succeed or fail for a given input. An introspective tool,
especially for certain tasks, could serve as a front-end to an LLM in a given
application.

As a step toward such a tool, we investigate aspects of math word problems
(MWPs) that can indicate the success or failure of ChatGPT on such problems.
We found that ChatGPT’s performance changes dramatically based on the
requirement to show its work, failing $20\%$ of the time when it provides work
compared with $84\%$ when it does not. Further several factors about MWPs can
lead to a higher probability of failure when compared with the prior,
specifically noting that the probability of failure increases linearly with
the number of addition and subtraction operations (across all experiments). We
also have released the dataset of ChatGPT’s responses to the MWPs to support
further work on the characterization of LLM performance. While there has been
previous work examining the LLM performance on MWPs [4], such work did not
investigate specific aspects that increase MWP difficulty nor did it examine
performance on ChatGPT in particular.

The remainder of this paper proceeds as follows. In Section 2, we describe our
methodology. Then we describe our results in Section 3. Using these
intuitions, we present baseline models to predict the performance of ChatGPT
in Section 4. This is followed by a discussion of related work (Section 5) and
future work (Section 6).

##  2 Methodology

MWP Dataset. In our study, we employed the DRAW-1K dataset [11, 12, 13] which
not only includes 1,000 MWPs with associated answers but also template
algebraic equations that one would use to solve such a word problem. As a
running example, consider the following MWP.

> One whole number is three times a second. If 20 is added to the smaller
> number, the result is 6 more than the larger.

We show ChatGPT’s (incorrect) response to this MWP in Figure 1. The DRAW-1K
dataset not only includes the correct answer, which in this case is $12$ and
$7$ but also includes template equations used to solve the problem. For our
running example, this consists of the equations $m-n=a-b$ and $c\times m-n=0$.
This information represents a symbolic representation of the problem which can
potentially be used to identify aspects that make such problems more
difficult.

Figure 1: ChatGPT’s response (Jan. 24, 2023) to MWP One whole number is three
times a second. If 20 is added to the smaller number, the result is 6 more
than the larger. In Step A it correctly identifies the set of equations needed
to solve the problem and correctly simplifies it in Step B. However, it fails
to correctly perform the algebraic operation in Step C (it should state
$2y=14$). This leads ChatGPT to obtain an incorrect result, returning $42$ and
$14$ instead of $21$ and $7$.

Entering Problems into ChatGPT at Scale. At the time of our study, OpenAI, the
maker of ChatGPT had not released an API. However, using the ChatGPT CLI
Python Wrapper111We used ChatGPT CLI Python Wrapper by Mahmoud Mabrouk, see
https://github.com/mmabrouk/chatgpt-wrapper we interfaced with ChatGPT
allowing us to enter the MWP’s at scale. For the first two experiments, we
would add additional phrases to force ChatGPT to show only the final answer.
We developed these additions to the prompt based on queries to ChatGPT to
generate the most appropriate phrase. However, we found in our third
experiment that this addition impacted results. We ran multiple experiments to
test ChatGPT’s ability with these problems.

  * •

January 2023 Experiment (No work). Our first experiment was run in early
January 2023 prior to OpenAI’s announcement of improved performance on
mathematical tasks on January 30,
2023222https://help.openai.com/en/articles/6825453-chatgpt-release-notes and
in this experiment we included the following statement as part of the prompt.

> Don’t provide any work/explanation or any extra text. Just provide the final
> number of answers for the previous question, with absolutely no other text.
> if there are two or more answers provide them as a comma separated list of
> numbers.

  * •

February 2023 Experiment (No work). Our second experiment was run in mid-
February 2023 after the aforementioned OpenAI announcement and also used a
prompt that would cause ChatGPT to show only the answer, however we found that
our original prompt led to more erratic behavior, so we modified the prompt
for this experiment, and used the following.

> Don’t provide any work/explanation or any extra text. Just provide the final
> number of answers for the previous question, with absolutely no other text.
> if there are two or more answers provide them as a comma separated list of
> numbers like: ’10, 3,’ etc; or if there is only 1 answer provide it like
> ’10’. Absolutely no other text just numbers alone. Just give me the numbers
> (one or more) alone. No full stops, no spaces, no words, no slashes,
> absolutely nothing extra except the 1 or more numbers you might have gotten
> as answers.

  * •

February 2023 Experiment (Showing Work). We also repeated the February
experiment without the additional prompt, thereby allowing ChatGPT to show all
its work. We note that in this experiment we used ChatGPT Plus which allowed
for faster response. At the time of this writing, ChatGPT Plus is only thought
to be an improvement to accessibility and not a different
model.333https://openai.com/blog/chatgpt-plus/

##  3 Results

The key results of this paper are as follows: (1.) the creation of a dataset
consisting of ChatGPT responses to the MWPs, (2.) identification of ChatGPT
failure rates ($84\%$ for January and February experiments with no work and
$20\%$ for the February experiment with work), (3.) identification of several
factors about MWPs relating to the number of unknowns and number of operations
that lead to a higher probability of failure when compared with the prior
(Figure 3), (4.) identification that the probability of failure increases
linearly with the number of addition and subtraction operations (Figure 5),
and (5.) identification of a strong linear relationship between the number of
multiplication and division operations and the probability of failure in the
case where ChatGPT shows its work.

Dataset. We have released ChatGPT’s responses to the 1,000 DRAW-1K MWP’s for
general use at https://github.com/lab-v2/ChatGPT_MWP_eval. We believe that
researchers studying this dataset can work to develop models that can combine
variables, operate directly on the symbolic template, or even identify aspects
of the template from the problem itself in order to predict LLM performance.
We note that at the time of this writing, collecting data at scale from
ChatGPT is a barrier to such work as API’s are not currently directly
accessible, so this dataset can facilitate such ongoing research without the
overhead of data collection.

Overall Performance of ChatGPT on DRAW-1K. As DRAW-1K provides precise can
complete answers for each problem, we classified ChatGPT responses in several
different ways and the percentage of responses in each case is shown in Figure
2.

  1. 1.

Returns all answers correctly. Here ChatGPT returned all answers to the MWP
(though it may round sometimes).

  2. 2.

Returns some answers correctly, but not all values. Here the MWP called for
more than one value, but ChatGPT only returned some of those values.

  3. 3.

Returns “No Solution.” Here ChatGPT claims there was no solution to the
problem. This was not true for any of the problems.

  4. 4.

Returns answers, but none are correct. Here ChatGPT returned no correct
answers (e.g., see Figure 1).

Throughout this paper, we shall refer to the probability of failure as the
probability of cases 3 and 4 above (considered together). In our February
experiment, we found that when ChatGPT omitted work, the percentages, as
reported in Figure 2 remained the same, though they differed significantly
when work was included. We also report actual numbers for all experiments in
Table 1. We note that the probability of failure increases significantly when
the work is not shown. However, when the work is included, ChatGPT obtains
performance in line with state-of-the-art models (i.e. EPT [18, 16]) which has
a reported $59\%$ accuracy while ChatGPT (when work is shown) has fully
correct (or rounded) answers $51\%$ of the time, but can be viewed as high as
$80\%$ if partially correct answers are included.

Figure 2: Overall results on the 1,000 MWPs in DRAW-1K based on ChatGPT’s response. Response Type | Jan. 2023 | Feb. 2023 | Feb. 2023
---|---|---|---
| (No work) | (No work) | (Showing work)
Returns answers, but none are correct | 831 | 830 | 186
Returns “No Solution” | 9 | 10 | 14
Returns all answers correctly | 135 | 134 | 513
Returns some answers correctly, but not all values | 25 | 26 | 287
Table 1: Number of responses for each ChatGPT Variant

Factors Leading to Incorrect Responses. We studied various factors from the
templated solutions provided for the MWP in the DRAW-1K dataset and these
included number of equations, number of unknowns, number of division and
multiplication operations, number of addition and subtraction operations, and
other variants derived from the metadata in the DRAW-1K dataset. We identified
several factors that, when present, cause ChatGPT to fail with a probability
greater than the prior (when considering the lower bound of a $95\%$
confidence interval). These results are shown in Figure 3. One interesting
aspect we noticed is that when the system would be required to show its work,
the number of unknowns present no longer seems to increase the probability of
failure (this was true for all quantities of unknowns in addition to what is
shown in Figure 3). Additionally, the number of multiplication and division
operations, while increasing the probability of failure greater than the prior
in the January experiment was not significant (based on $95\%$ confidence
intervals) in the February experiment (when work was not shown) - possibly a
result of OpenAI’s improvements made at the end of January. However, there was
a significant relationship between the number of multiplication and division
operations and failure when work was shown. In fact, we found a strong linear
relationship ($R^{2}=0.802$) for this relationship in the case where work was
shown.

Figure 3: Aspects of MWPs that led to ChatGPT failure more often than the
prior ($95\%$ confidence intervals shown). Figure 4: Additional finding
specific to the February, 2023 experiment where ChatGPT displayed its work
relating number of multiplications to probability of failure, $R^{2}=0.802$,
$95\%$ confidence intervals.

Correlation of failure with additions and subtractions. Previous work has
remarked on the failure of LLM’s in multi-step reasoning [1, 4]. In our study,
we identified evidence of this phenomenon. Specifically, we found a strong
linear relationship between the number of addition and subtraction operations
with the probability of failure ($R^{2}=0.821$ for the January experiment,
$R^{2}=0.870$ for the February experiment and $R^{2}=0.915$ when work was
shown). We show this result in Figure 5. It is noteworthy that the
relationship existed in all of our experiments, and seemed to be strengthened
when ChatGPT included work in the result.

Figure 5: Increase in probability of an incorrect response as a function of
the number of addition operations (prior probability shown with dashed line,
$95\%$ confidence intervals, linear regression with $R^{2}=0.821$ for January,
$R^{2}=0.870$ for February without showing work and $R^{2}=0.915$ for February
with showing work).

##  4 Performance Prediction Baselines

The results of the previous section, in particular, the factors indicating a
greater probability of failure (e.g. Figures 3-5), may indicate that the
performance of ChatGPT can be predicted. In this section, we use features
obtained from the equations associated with the MWPs to predict performance.
Note that here we use ground-truth equations to derive the features, so the
models presented in this section are essentially using an oracle - we leave
extracting such features from equations returned by ChatGPT or another tool
(e.g., EPT [18]) to future work. That said, as these features deal with counts
of operations, unknowns, and equations, a high degree of accuracy in creating
the equations would not be required to faithfully generate such features.

Following the ideas of machine learning introspection [9, 10], we created
performance prediction models using random forest and XGBoost. We utilized
scikit-learn 1.0.2 and XGBoost 1.6.2 respectively. In our experiments, we
evaluated each model on each dataset using a five-fold cross-validation and
report average precision and recall in Table 2 (along with F1 computed based
on those averages). In general, our models were able to provide higher
precision than random on predicting incorrect answers for both classifiers.
Further, XGBoost was shown to be able to provide high recall for predicting
correct responses. While these results are likely not suitable for practical
use, they do demonstrate that the features extracted provide some amount of
signal to predict performance and provide a baseline for further study.

Version of | Model | Incorr. | Incorr. | Incorr. | Corr. | Corr. | Corr.
---|---|---|---|---|---|---|---
ChatGPT | Type | Prec. | Recall | F1 | Prec. | Recall | F1
Jan. | RF | 0.90 | 0.88 | 0.89 | 0.34 | 0.41 | 0.37
(No work) | XGBoost | 0.95 | 0.22 | 0.36 | 0.16 | 0.93 | 0.26
Feb. | RF | 0.94 | 0.89 | 0.91 | 0.47 | 0.63 | 0.54
(No work) | XGBoost | 0.98 | 0.35 | 0.51 | 0.18 | 0.95 | 0.31
Feb. | RF | 0.78 | 0.69 | 0.73 | 0.74 | 0.82 | 0.78
(Showing work) | XGBoost | 0.77 | 0.59 | 0.67 | 0.69 | 0.83 | 0.75
Table 2: Performance Prediction Baseline Models using Ground Truth Equations

##  5 Related Work

The goal of this challenge dataset is to develop methods to introspect a given
MWP in order to identify how an LLM (in this case ChatGPT) will perform.
Recent research in this area has examined MWPs can be solved by providing a
step-by-step derivation [14, 15, 16, 17]. While these approaches provide
insight into potential errors that can lead to incorrect results, this has not
been studied in this prior work. Further, the methods of the aforementioned
research are specific to the algorithmic approach. Work resulting from the use
of our challenge dataset could lead to solutions that are agnostic to the
underlying MWP solver - as we treat ChatGPT as a black box. We also note that,
if such efforts to introspect MWPs are successful, it would likely complement
a line of work dealing with “chain of thought reasoning” for LLMs [5, 6] which
may inform better ways to generate MWP input into an LLM (e.g., an MWP with
fewer additions may be decomposed into smaller problems). While some of this
work also studied LLM performance on Math Word Problems (MWPs), it only looked
at how various prompting techniques could improve performance rather than
underlying characteristics of the MWP that leads to degraded performance of
the LLM.

##  6 Future Work

Understanding the performance of commercial black-box LLMs will be an
important topic as they will likely become widely used for both commercial and
research purposes. Further future directions would also include an examination
of ChatGPT performance on datasets other MWPs [13], investigating ChatGPT’s
nondeterminism, and exploring these studies on upcoming commercial LLM’s to be
released by companies such as Alphabet and Meta.

###### Acknowledgements.

Some of the authors have been funded by the ASU Fulton Schools of Engineering.

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# Reasoning before Comparison: LLM-Enhanced Semantic Similarity Metrics for
Domain Specialized Text Analysis

Shaochen Xu  School of Computing, University of Georgia  Zihao Wu  School of
Computing, University of Georgia  Huaqin Zhao  School of Computing, University
of Georgia  Peng Shu  School of Computing, University of Georgia  Zhengliang
Liu  School of Computing, University of Georgia  Wenxiong Liao  School of
Computer Science and Engineering, South China University of Technology
Sheng Li  School of Data Science, University of Virginia  Andrea Sikora
Department of Clinical and Administrative Pharmacy, University of Georgia
College of Pharmacy  Tianming Liu  School of Computing, University of Georgia
Xiang Li  Massachusetts General Hospital and Harvard Medical School

###### Abstract

In this study, we leverage LLM to enhance the semantic analysis and develop
similarity metrics for texts, addressing the limitations of traditional
unsupervised NLP metrics like ROUGE and BLEU. We develop a framework where
LLMs such as GPT-4 are employed for zero-shot text identification and label
generation for radiology reports, where the labels are then used as
measurements for text similarity. By testing the proposed framework on the
MIMIC data, we find that GPT-4 generated labels can significantly improve the
semantic similarity assessment, with scores more closely aligned with clinical
ground truth than traditional NLP metrics. Our work demonstrates the
possibility of conducting semantic analysis of the text data using semi-
quantitative reasoning results by the LLMs for highly specialized domains.
While the framework is implemented for radiology report similarity analysis,
its concept can be extended to other specialized domains as well.

##  1 Introduction

The analysis of medical texts is a key component of healthcare informatics,
where the accurate comparison and interpretation of documents can
significantly impact patient care and medical research. Traditionally, this
analysis has leveraged lexical comparison metrics such as ROUGE (Recall-
Oriented Understudy for Gisting Evaluation) [1] and BLEU (Bilingual Evaluation
Understudy) [2], which have become standard tools in the evaluation of text
similarity within the domain of natural language processing (NLP). ROUGE and
BLEU were initially designed to assess the quality of automatic summarization
and machine translation respectively, by measuring the overlap of n-grams
between the generated texts and reference texts.

While these metrics have been instrumental in advancing NLP applications,
their application in medical text analysis reveals inherent limitations.
Specifically, ROUGE and BLEU focus predominantly on surface-level lexical
similarities, often overlooking the deep semantic meanings and clinical
implications embedded within medical documents. This gap in capturing the
essence and context of medical language presents a significant challenge in
leveraging these metrics for meaningful analysis in healthcare.

Recognizing these limitations, this research proposes a novel methodology that
employs GPT-4, a state-of-the-art large language model, for a more
sophisticated analysis of medical texts. GPT-4’s advanced understanding of
context and semantics [3, 4, 5] offers an opportunity to transcend the
boundaries of traditional lexical analysis, enabling a deeper, more meaningful
comparison of medical documents [6, 7]. This approach not only addresses the
shortcomings of ROUGE and BLEU but also aligns with the evolving needs of
medical data analysis, where the accurate interpretation of texts is
preeminent.

Despite the proliferation of advanced language models in the general domain,
with models like Google’s Gemini [8] claiming superior performance to GPT-4
through comprehensive benchmark comparisons, the specialized domain of medical
text analysis presents unique challenges that remain largely unaddressed. The
intricacies of medical language, characterized by its dense terminologies,
expressions, and implications, necessitate models that are not only adept at
general language understanding but are also finely tuned to grasp the
subtleties of medical discourse. Unlike the broader NLP field where
benchmarking new models against established standards like GPT-4 is common,
the medical field lacks a similar benchmarking framework to rigorously
evaluate the performance of specialized LLMs. This gap highlights a need for
developing methodologies that can effectively compare the language generated
by AI models directly to ground truth data within medical contexts. Therefore,
this study aims to establish a methodological framework that bridges this gap,
offering a way to directly compare generated language to the authentic text
data found in medical documents. Such a framework would not only enhance our
understanding of how well current LLMs perform in specialized domains but also
set a precedent for evaluating future models in the context of healthcare
informatics, contributing significantly to the advancement of precision
medicine and evidence-based clinical practices

An essential aspect of our methodology is the integration of a "human-in-the-
loop" approach for evaluating the results. This framework enhances the
interpretability of AI-generated outputs, making it easier for medical
professionals to engage with and derive insights from complex data analyses.
By converting GPT-4’s analysis into generic labels for categorization, we
present a more accessible and interpretable medium for comparison. This
innovation significantly simplifies the task for human reviewers, who can now
assess the similarity and relevance of documents through label comparisons
rather than exhaustive text reviews, aligning with the principles of
explainable AI (XAI) and enhancing user trust and understanding in AI
applications [9, 10].

The significance of this study extends beyond addressing the limitations of
existing lexical metrics; it proposes a multi-dimensional, quantifiable
approach for text comparison that meets the granular and diverse needs of the
medical field. By integrating the computational strengths of GPT-4 with the
insights of human expertise, this research contributes to the advancement of
precision medicine and evidence-based practice, promising to revolutionize
medical data analysis and healthcare informatics.

##  2 Related Works

###  2.1 Text Comparison Methods in Medical Text Analysis

In healthcare environments, imaging examinations including ultrasonography,
magnetic resonance imaging (MRI), and computed tomography (CT) scans, are
pivotal for patient assessment and care. To enhance the accuracy and quality
of imaging diagnostics, it is essential to meticulously review and contrast
pre-existing reports of imaging and pathology examinations that pertain to
identical anatomical sites, as recorded in electronic medical records (EMRs).
This retrieval process, however, is notably labor-intensive. Consequently,
scholars in the field have dedicated efforts to develop a variety of models
and metrics aimed at quantifying text similarities, facilitating a more
efficient and systematic approach to comparing diagnostic reports[11, 12, 1,
13, 14, 15, 16, 17].

Researchers have defined numerous metrics to measure medical text similarity,
encompassing perspectives from traditional linguistics, and syntactic angles,
to similarities in text vector representations. The Bilingual Evaluation
Understudy (BLEU) metric, while primarily used for evaluating machine
translation quality, can also be applied in comparing medical texts generated
from different sources or translation systems, particularly for assessing
translation accuracy[11]. The Recall-Oriented Understudy for Gisting
Evaluation (ROUGE) metric is extensively utilized in automatic text
summarization, especially for creating summaries of medical research reports
or patient records, aiding in evaluating the consistency between auto-
generated summaries and human-written counterparts[12, 1]. Moreover, the
Metric for Evaluation of Translation with Explicit Ordering (METEOR) provides
a more precise assessment in medical text comparisons, such as patient record
descriptions versus medical database entries, by considering synonyms and
sentence structures, catering to tasks requiring high linguistic
understanding[13, 14]. Levenshtein distance is employed to assess and correct
spelling errors in medical texts, like patient record entry or typographical
errors in medical literature, enhancing accuracy in text comparisons[15, 13].
Cosine similarity is used in medical literature retrieval and similar case
finding, comparing document vector representations to identify thematically or
content-wise similar documents, valuable for researchers and clinicians
seeking related studies or cases[18, 19, 16]. Lastly, the Word Mover’s
Distance (WMD), is another metric that captures deep semantic information and
proves especially valuable in comparing medical texts, such as clinical case
report similarities, identifying semantically similar but differently
expressed terms, crucial for handling the rich and varied expressions in
medical terminology[17, 20].

Furthermore, there is more research exploring potential combination
applications between LLMs and healthcare[21, 22, 23, 24, 25]. [26] aims to
evaluate GPT-4V across a diverse set of capabilities required for real-world
medical imaging tasks in various modalities such as X-ray, MRI, CT, and
microscopy images. [27] develops an LLM specifically for ophthalmology, called
Ophtha-LLaMA2 which exhibits satisfying accuracy and efficiency in ophthalmic
disease diagnosis. [28] proposes the ChatRadio-Valuer, a tailored model for
automatic radiology report generation that learns generalizable
representations and provides a basis pattern for model adaptation in
sophisticated analysts’ cases.

##  3 Method

In this study, we aim to leverage GPT-4 for the comparative analysis of
medical texts at a higher semantic level. This method separates from
conventional lexical comparison tools, relying on the LLM’s ability to
interpret, reason, and categorize medical texts based on their content (See
Figure 1).

Figure 1: Overview of the prompt and response conversation between the user
and GPT-4 to generate a list of custom labels for any given medical text. (1)
The left side showcases the entire conversational flow between the user and
GPT-4 to generate the desired labels. (2) While the right side displays the
products of said conversation from GPT-4’s response.

###  3.1 Text Identification

The initial step of the proposed framework focuses on the task of text
identification, where we present GPT-4 with anonymized radiology reports,
without providing any contextual clues or background information. This step is
essential, as it assesses GPT-4’s inherent capability to accurately discern
the type and nature of the given documents based solely on textual content.
The ability of GPT-4 to perform this task is not taken for granted; rather,
drawing on the work by Avati et al. [29], who highlighted the role of precise
data categorization in healthcare data analysis, our study explores the
applicability of LLMs for similar tasks in medical informatics.

The significance of this stage lies in its potential to validate the utility
of LLMs in the field of medical informatics, where precision and context are
predominant. By successfully identifying medical texts, GPT-4 would
demonstrate a critical level of contextual interpretation, which goes beyond
basic keyword recognition or surface-level analysis. This step, therefore,
sets the stage for more complex analytical tasks, such as the generation of
identification tasks and labeling, which are predicated on the model’s initial
identification accuracy.

###  3.2 Task Generation

Following the identification of medical texts by GPT-4, the next step in our
methodology is the generation of tasks by the model. This step involves asking
GPT-4 to lay out the specific criteria or features utilized for recognizing
and classifying the medical texts. This approach aligns with the principles of
explainable AI (XAI) [9], which emphasizes making AI’s decision-making
processes transparent and understandable to humans. In their work, Arrieta et
al. underscores the importance of XAI in enhancing the interpretability of AI
models, thereby making their operations and decisions more accessible to
users.

The tasks generated by GPT-4 are expected to cover a broad spectrum,
reflecting the model’s understanding of medical texts. These tasks might
include identifying key medical terminologies and their relevance to certain
conditions or recognizing the structure of the reports and the implications of
specific diagnostic criteria. Importantly, these tasks can offer tailored
utility to different medical professionals. For instance, tasks related to
detailed findings and diagnostic interpretations could be particularly
valuable to radiologists, who require a deep understanding of imaging reports
to make accurate diagnoses. On the other hand, tasks that assess the urgency
or emergency level of cases, based on textual cues within the reports, could
be crucial for nurses and emergency department staff. This differentiation in
task relevance underscores the potential of GPT-4 to support a wide range of
clinical activities and decision-making processes.

This aspect of task generation draws inspiration from the work of Holzinger et
al. [10], who advocate for interactive machine learning (iML) in enhancing the
collaboration between humans and AI, especially in complex domains like
healthcare. iML focuses on leveraging human expertise to improve AI models’
learning and interpretability. By involving "human-in-the-loop" throughout
different stages and generating tasks that cater to the specific needs and
expertise of different medical professionals, GPT-4 could significantly
contribute to the development of AI tools that are not only advanced in their
language understanding capabilities but also versatile in their application
across the healthcare sector.

###  3.3 Label Creation

The label creation phase, following task generation by GPT-4, is pivotal for
organizing the AI-identified features of medical texts into coherent and
actionable labels. This process is not just about leveraging GPT-4’s advanced
language capabilities but also about enhancing the labels’ usability and
interpretability in clinical settings. By categorizing complex medical
information into concise labels, we facilitate a more intuitive interface for
medical professionals to engage with AI-generated insights.

An integral aspect of this phase is the incorporation of the "human-in-the-
loop" methodology, which significantly enriches the label creation process.
This approach allows for the direct involvement of medical professionals in
the review and refinement of the generated labels. The practical advantage of
this is twofold. First, it makes it easier for human reviewers to discern
differences and similarities between sets of labels, rather than navigating
the complexities of full medical texts. This comparative simplicity can be
crucial in clinical settings, where efficiency and accuracy are imperative.
Second, involving human expertise at this stage ensures the labels are not
only accurate but also clinically relevant and aligned with medical
terminologies and practices. The generated labels thus serve as a bridge
between GPT-4’s computational analysis and the practical knowledge and needs
of healthcare practitioners. Such an approach aligns with the work of Caruana
et al. [30], who emphasized the importance of interpretability in medical AI
applications, suggesting that models should be developed with a focus on their
eventual use in patient care.

Furthermore, the systematic categorization provided by these labels supports
the broader goal of integrating AI tools into healthcare workflows in a manner
that enhances, rather than complicates, decision-making processes. By ensuring
that labels are both generated and validated with human insight, the label
creation phase becomes a fundamental step towards developing AI applications
that are not only powerful in analysis but also practical and trustworthy for
routine clinical use.

###  3.4 Categorization and Comparison

Once GPT-4 has generated and refined a set of labels based on its pre-existing
knowledge base or potentially through a collaborative "human-in-the-loop"
approach, the subsequent phase involves applying these labels to categorize
new medical texts. This process is important for evaluating the effectiveness
and applicability of the labels in real-world medical settings. The
categorization of medical texts using AI-generated labels represents a
significant shift from traditional text analysis methods, switching towards a
more refined understanding of medical documents.

The primary goal of this phase is to assess how well the labels facilitate the
grouping of texts based on shared characteristics, which may include
diagnostic findings, treatment recommendations, or patient management
strategies. This approach allows for a more granular and semantic analysis of
medical texts, beyond what is achievable through standard lexical analysis
tools. The comparison of texts based on these labels can help identify
patterns, similarities, and differences across a wide array of medical
documents, providing valuable insights for clinical research, patient care,
and healthcare administration.

##  4 Experiment

###  4.1 MIMIC-CXR

Our experiment utilizes the MIMIC-CXR dataset [31] from Beth Israel Deaconess
Medical Center, which includes de-identified chest radiograph images and
detailed radiology reports. We specifically used the radiology reports
section, with its rich annotations and ground truth labels, to test our text
comparison methodology using GPT-4. These labels, curated by medical
professionals, provide a high-accuracy benchmark for evaluating GPT-4’s
performance in identifying and categorizing report content.

To ensure our analysis focused on reports offering the most clinically
relevant information for GPT-4 to analyze, we excluded reports labeled solely
as "No Finding" by CheXpert and NegBio. This decision was based on the
rationale that such reports, while indicating the absence of specific
findings, often lack the detailed clinical information necessary for GPT-4 to
generate insightful labels. Consequently, our dataset curation aimed at
selecting reports with descriptive labels to fully utilize GPT-4’s
capabilities in generating clinically significant labels, thereby optimizing
the utility of our methodology for medical text analysis.

###  4.2 Utilizing GPT-4 for Label Generation

Our experiment (See Figure 2) began by presenting GPT-4 with four randomly
chosen radiology reports from the MIMIC-CXR dataset to test its text
recognition capabilities. GPT-4 accurately identified these as chest radiology
reports, showcasing its potential for sophisticated text analysis. We then
advanced to having GPT-4 generate tasks and corresponding generic labels for
these tasks, relying solely on its pre-existing knowledge, to see how well it
could classify texts within the medical domain without additional information.

After generating tasks and labels, we conducted manual reviews to select those
that best matched the findings-based labeling method used in the MIMIC-CXR
dataset. This ensured the generated labels closely mirrored the dataset’s
ground truth labels, maintaining consistency and relevance.

Next, we applied this refined methodology to new reports from the MIMIC-CXR
dataset, directing GPT-4 to generate labels for each using the findings-based
approach. This procedure was replicated for 500 reports, allowing us to assess
GPT-4’s label generation capabilities across a variety of reports. This
approach provided us with a well-rounded collection of GPT-4 generated labels,
confirming the model’s effectiveness in producing clinically relevant labels
and highlighting the consistency of our methodology.

Figure 2: Computational pipeline of the similarity difference calculation
between two radiology reports from the MIMIC-CXR dataset used in our
experimental settings.

###  4.3 Obtaining Ground Truth Similarities

In our study, we utilized a subset of 500 radiology reports from the MIMIC-CXR
dataset. To facilitate a comprehensive analysis of report similarities, these
reports were evenly divided into two groups, ensuring that each report from
one group would be systematically compared with every report in the opposing
group. This division resulted in a total of 62,500 unique comparisons,
providing a robust foundation for evaluating the effectiveness of our proposed
methodology in identifying similarities between radiology reports.

The core of our ground truth similarity assessment lies in the conversion of
labeled data for each report into a vector representation. The labels provided
by CheXpert and NegBio, which include a range of pathologies and findings such
as Atelectasis, Cardiomegaly, and Pleural Effusion, were encoded into vectors
comprising four possible values: 1.0 (positive mention), 0.0 (negative
mention), -1.0 (uncertain or ambiguous mention), and missing. To accommodate
the computational requirements of our similarity analysis, the missing values,
which signify the absence of a mention of the label in the report, were
replaced with ’-2’. This adjustment was critical for ensuring that each aspect
of the labeling—positive, negative, uncertain, and unmentioned—was distinctly
represented in the vector, allowing for a distinctive comparison between
reports.

We then calculated the ground truth similarity between report pairs using the
cosine similarity metric, which quantifies the similarity between two vectors,
with scores ranging from -1 to 1. This approach provided a measurable
similarity score based on the clinical findings and diagnoses as categorized
by CheXpert and NegBio labels, offering a detailed assessment of our proposed
methodology’s effectiveness.

###  4.4 Computing Similarities for GPT-4 Generated Labels

To evaluate the similarities between radiology reports based on labels
generated by GPT-4, we employed the "all-mpnet-base-v2" model [32], a state-
of-the-art sentence embedding framework designed to capture deep semantic
meanings. This model was specifically chosen for its ability to understand and
process the complex medical terminology and varied expressions found in
radiology reports, translating them into a high-dimensional vector space where
semantic similarities could be quantitatively assessed.

After converting the labels into normalized semantic embeddings using "all-
mpnet-base-v2," we calculated the cosine similarity between label vectors from
two distinct reports within our dataset, covering 62,500 comparisons. This
approach quantified the semantic similarity between report label pairs,
providing a detailed evaluation of the labels’ relevance and coherence as
generated by GPT-4.

###  4.5 Evaluation of GPT-4 Generated Labels Versus Traditional Metrics

In our comprehensive analysis, we extended the evaluation of radiology report
similarities by incorporating traditional NLP metrics, including ROUGE-1,
ROUGE-2, ROUGE-L, and BLEU scores, for all 62,500 unique comparisons between
the reports. These metrics, widely used in the assessment of text generation
and summarization tasks, provided a baseline for lexical similarity that
focuses on the overlap of n-grams between the texts for ROUGE metrics and the
precision of matched sequences in the BLEU score. By applying these metrics,
we aimed to quantify the lexical coherence and similarity between pairs of
radiology reports based on their textual content.

Following the computation of traditional similarity scores, we began the
comparison procedures. Here, we calculated the difference between the scores
obtained from ROUGE and BLEU metrics and the semantic similarities derived
from the GPT-4 generated labels against the ground truth similarities
previously established. This comparative analysis was essential for
understanding how the GPT-4 generated labels’ semantic depth and relevance
measure against the lexical similarity benchmarks provided by ROUGE and BLEU
scores.

To synthesize our findings, we calculated the mean of all differences across
the comparisons for each method (ROUGE-1, ROUGE-2, ROUGE-L, BLEU, and GPT-4
generated labels) against ground truth similarities. This aggregation provided
a holistic view of the variance between traditional lexical similarity metrics
and the semantically driven approach offered by GPT-4 generated labels. By
computing the average differences, we aimed to discern patterns and insights
into the effectiveness of GPT-4’s labels in capturing the true similarity
between radiology reports, as compared to conventional NLP metrics.

##  5 Results

Our analysis focused on comparing predicted similarities of radiology report
pairs using various methods against ground truth (GT) similarities, derived
from a dataset encompassing 62,500 text pair comparisons. The comparison
leveraged several metrics, including GPT_sim (GPT-4 generated similarities),
ROUGE-1 F1, ROUGE-2 F1, ROUGE-L F1, and BLEU scores, to evaluate their
performance in aligning with the GT derived from CheXpert and NegBio
annotations.

A visual representation of our findings is illustrated in Figure 3 that maps
the predicted similarities against the GT similarities. In this figure, a
dashed line represents the ideal prediction-GT match, spanning from the 5th to
the 95th percentile range of GT similarities. A closer proximity of the data
points to this dashed line indicates a higher correspondence with the GT. For
enhanced clarity and focus on significant trends, only hexagonal bins
aggregating more than 100 observations are displayed.

Figure 3: Comparison of predicted similarities using various methods across
the 62,500 text pairs. The dashed line denotes the ideal prediction-ground
truth (GT) match across the 5th to 95th percentile range of GT similarities. A
closer alignment to the dashed line signifies a greater correspondence with
the GT. For clarity, only hexagonal bins with more than 100 observations are
displayed. The results demonstrate that GPT_sim exhibits the highest degree of
alignment with the GT.

The results from our analysis are summarized in Table 1, which shows the mean
similarity scores for each comparison method against both CheXpert and NegBio
annotations. Notably, GPT_sim exhibits the highest degree of alignment with
the GT, achieving scores of 0.1768 and 0.1793 against CheXpert and NegBio,
respectively. This contrasts with the higher mean scores observed for ROUGE
and BLEU metrics, indicating a greater deviation from the GT. Specifically,
ROUGE_1_F1, ROUGE_2_F1, and ROUGE_L_F1 scores ranged from 0.3654 to 0.5827,
while BLEU scores were close to 0.6, all of which signify less correspondence
with the GT compared to GPT_sim.

These findings convey GPT_sim’s superior performance in mirroring the GT
similarities, highlighting its efficacy in capturing the semantic nuances of
medical texts. The marked discrepancy between GPT_sim and traditional NLP
metrics in alignment with GT shows the potential of advanced language models
in enhancing the accuracy and relevance of medical document analysis. This
comparison not only validates the proposed methodology’s effectiveness but
also illustrates the limitations of conventional metrics in reflecting the
true semantic similarities within medical texts, advocating for the broader
adoption of AI-driven methods in healthcare informatics.

Table 1: Mean Differences in Similarity Scores Comparison | CheXpert | NegBio
---|---|---
GPT_sim | 0.1768 | 0.1793
ROUGE_1_F1 | 0.3654 | 0.3714
ROUGE_2_F1 | 0.5767 | 0.5827
ROUGE_L_F1 | 0.3765 | 0.3825
BLEU | 0.5991 | 0.5941

##  6 Limitations and Future Work

This study, while pioneering in its approach to leveraging advanced language
models for the analysis of medical texts, encounters several limitations that
merit consideration. A primary limitation arises from the hypothesized ’human-
in-the-loop’ (HITL) component of our methodology. Despite the theoretical
framework laid out for integrating medical professionals’ insights into the
AI-generated label evaluation process, the actual implementation of this
aspect was beyond the scope of our current research. Consequently, the
potential enhancements in accuracy, relevance, and clinical utility
anticipated from direct human expert involvement remain speculative at this
stage. The absence of medical professionals’ engagement in refining and
validating AI-generated labels presents a gap between the envisioned and
realized capabilities of our methodology, potentially affecting the robustness
of our findings and their applicability in clinical settings.

Furthermore, the scope of our research was confined to the analysis of chest
x-ray radiology reports, limiting the generalizability of our findings across
the vast spectrum of medical documentation. While chest x-rays constitute a
significant subset of medical imaging, the medical field encompasses a wide
array of data types, each with its unique linguistic and clinical
characteristics. Our methodology’s effectiveness in interpreting and
categorizing texts from other medical domains, such as pathology reports,
clinical notes, or imaging reports from other modalities, remains untested.
This limitation underscores the need for further research to explore the
applicability and scalability of our proposed method across different types of
medical data, ensuring its relevance and utility in a broader healthcare
context.

Addressing these limitations is essential for advancing the research agenda
set forth by this study. Future work should aim to operationalize the HITL
framework with the active participation of medical professionals, thereby
empirically testing and refining the AI-generated labels’ clinical validity.
Additionally, extending the analysis to encompass a wider variety of medical
texts will be crucial for assessing the method’s versatility and effectiveness
in diverse healthcare informatics applications. By confronting these
challenges, subsequent research can build on our foundational work, enhancing
the integration of AI in medical text analysis and contributing to the
advancement of precision medicine and evidence-based clinical practices.

##  7 Conclusion

Our investigation into the semantic analysis of medical texts, particularly
radiology reports from the MIMIC-CXR dataset, has indicated the potential of
employing advanced language models like GPT-4, coupled with the semantic
embedding capabilities of "all-mpnet-base-v2". This study’s approach surpasses
the conventional boundaries set by lexical comparison metrics such as ROUGE
and BLEU, which, despite their utility in broad NLP applications, fall short
of adequately capturing the nuanced semantic depths necessary for meaningful
medical text analysis.

The methodological framework developed herein—leveraging GPT-4 for the
generation of clinically relevant labels and employing semantic embeddings for
comprehensive similarity assessments—has demonstrated a significant
enhancement in the ability to discern semantic similarities between medical
documents. Our findings, derived from extensive comparisons across a curated
subset of 500 radiology reports, reveal that the labels generated by GPT-4,
when analyzed through semantic embeddings, align more closely with the
clinical ground truth than the traditional NLP metrics. This alignment not
only indicates the superiority of our proposed method in capturing the essence
of medical texts but also highlights the potential of such AI-driven
approaches to revolutionize healthcare informatics by facilitating a deeper
understanding of clinical documentation.

However, the study acknowledges certain limitations, including the absence of
direct medical professional involvement in the label evaluation process and
the focus solely on chest radiology reports. These limitations delineate
avenues for future research, particularly emphasizing the need for integrating
"human-in-the-loop" methodologies to enhance label accuracy and relevance, as
well as expanding the analysis to encompass a broader spectrum of medical
documents.

In conclusion, the outcomes of this study advocate for a shift in the
application of AI within medical text analysis, illustrating the benefits of
moving towards more semantically aware models. By combining the computational
strengths of GPT-4 with the insights of human expertise, our research
contributes an important step forward in the advancement of precision medicine
and evidence-based clinical practices. As we continue to refine these
methodologies and explore their application across wider domains of medical
data, the potential to greatly improve patient care through enhanced data
analysis and interpretation becomes increasingly apparent, marking a new
chapter in the intersection of AI and healthcare.

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