# Exploring ChatGPT’s Empathic Abilities

Kristina Schaaff, Caroline Reinig, Tim Schlippe  IU International University
of Applied Sciences
Erfurt, Germany
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Empathy is often understood as the ability to share and understand another
individual’s state of mind or emotion. With the increasing use of chatbots in
various domains, e.g., children seeking help with homework, individuals
looking for medical advice, and people using the chatbot as a daily source of
everyday companionship, the importance of empathy in human-computer
interaction has become more apparent. Therefore, our study investigates the
extent to which ChatGPT based on GPT-3.5 can exhibit empathetic responses and
emotional expressions. We analyzed the following three aspects: (1)
understanding and expressing emotions, (2) parallel emotional response, and
(3) empathic personality. Thus, we not only evaluate ChatGPT on various
empathy aspects and compare it with human behavior but also show a possible
way to analyze the empathy of chatbots in general. Our results show, that in
91.7% of the cases, ChatGPT was able to correctly identify emotions and
produces appropriate answers. In conversations, ChatGPT reacted with a
parallel emotion in 70.7% of cases. The empathic capabilities of ChatGPT were
evaluated using a set of five questionnaires covering different aspects of
empathy. Even though the results indicate that the empathic abilities of
ChatGPT are still below the average of healthy humans, the scores are better
than those of people who have been diagnosed with Asperger syndrome / high-
functioning autism.

###### Index Terms:

empathy, chatbot, ChatGPT, emotions

##  I Introduction

Chatbots have become a common tool in daily life over recent years [1]. These
systems can simulate human-like conversations with users [2], provide
assistance [3], information [4], and emotional support [5]. Among these,
OpenAI’s ChatGPT has emerged as one of the most widely used chatbots.
According to [6], ChatGPT reached over one million users in less than a week
after launch. Users include children seeking help with homework, people
seeking medical advice and people who use it as a daily source of
companionship.

Empathy is a human concept that is important in social interaction. Research
shows that empathy prevents individuals from displaying aggression toward
others [7] and helps to prevent children and adolescents from exhibiting
antisocial behavior [8]. Also, in chatbots, empathy has shown to be a crucial
trait in its acceptance by users [1].

[9] conducted the Short Dark Triad (SD-3) questionnaire [10] on the model
GPT-3. The results showed a psychopathy score exceeding human results,
indicating a lack of empathy. However, there is—to the best of our
knowledge—no study focusing on empathy in ChatGPT, which is based on the newer
model GPT-3.5. Consequently, this paper addresses this gap by exploring
ChatGPT’s empathic abilities.

Empathy is often understood as the ability to share and understand another
individual’s state of mind or emotion [11]. Therefore, as the first part of
the study, we tested ChatGPT’s ability to understand and express emotions.
Afterwards, we analyzed ChatGPT’s ability to express parallel emotional
responses, a subcategory of empathy [12]. Finally, we evaluated ChatGPT’s
level of empathy in various aspects using psychologically acknowledged
questionnaires.

To summarize, in this paper, we investigated ChatGPT’s emotional and empathic
abilities across three aspects: (1) understanding and expressing emotions, (2)
parallel emotional response, and (3) empathic personality.

In the following section, we will describe related work regarding ChatGPT, the
definition of empathy, and measuring empathy in chatbots. In Section III, we
will explain how we evaluated ChatGPT’s ability to understand and generate
emotions. Section IV describes our experiments to assess ChatGPT’s parallel
emotional responding capabilities. Additionally, we investigated ChatGPT’s
empathic capabilities with standardized questionnaires to assess empathy,
which we will show in Section V. We conclude our work in Section VI and
suggest further steps.

##  II Related Work

###  II-A ChatGPT

ChatGPT is a state-of-the-art chatbot developed by OpenAI that can produce
natural language text when given a prompt or context [13]. This versatile tool
can be employed in numerous fields, including education [13], medicine [14],
and language translation [15].

The chatbot is based on the large language model GPT-3.5 and was fine-tuned
using reinforcement learning from human feedback [16]. This approach allows
the model to grasp the meaning and intention behind user queries, leading to
relevant and helpful responses. To ensure safety and prevent the generation of
inappropriate or factually incorrect text, the training of ChatGPT was
enhanced by incorporating a large dataset of human-human and human-chatbot
conversations. OpenAI has not released any official information about the
exact amount of training data of ChatGPT, but the previous model GPT3 with 175
billion parameters was already significantly larger than other language models
like BERT, RoBERTA, or T5 and was trained with 499 billion crawled tokens
(i.e., subword units) [17]. By learning the intricacies and nuances of human
language through this extensive dataset, ChatGPT can produce highly realistic
text almost indistinguishable from human writing [18].

###  II-B Definition of Empathy

Empathy is a crucial component of effective communication, especially in
social interactions, as it allows humans to understand and share another
person’s feelings [19]. However, there is no consensus on the definition of
empathy [20]. One possible definition is to distinguish between cognitive and
affective empathy [20]. Cognitive empathy is the ability to understand and
identify another individual’s thoughts, feelings, and perspectives without
necessarily experiencing the same emotions, i.e., the capability of mental
perspective taking. It involves the capacity to recognize and interpret social
cues, facial expressions, body language, and verbal communication to
comprehend and infer the mental and emotional states of others [21]. Affective
empathy, on the other hand, facilitates a deeper connection and understanding
with others and involves a more visceral and personal connection to another’s
emotions [22]. Affective empathy can be divided into parallel emotional
response and reactive emotional response. Parallel emotional responses involve
responding with the same emotion as the other individual, while reactive
emotional responses go beyond matching emotions, such as sympathy or
compassion [12].

In our work, we evaluated ChatGPT’s understanding and expression of emotions.
Next, we analyzed its parallel emotional response. Finally, we covered the
other aspects of empathy with standardized questionnaires to evaluate empathy.

###  II-C Measuring Empathy in Chatbots

While standardized metrics exist to measure empathy in individuals, there are
currently no standardized or valid methods for measuring empathy in chatbots
[23]. A possible solution is to evaluate a chatbot’s level of empathy by human
evaluation, such as A/B tests or human ratings [24]. In A/B tests, the
annotator chooses which response is more empathic, often used when comparing
the level of empathy between two models. In human ratings, the annotator
chooses the level of empathy based on a scale.

Another way is to conduct a feature- or system-level evaluation instead. The
feature-level evaluation involves assessing each component and capability of a
chatbot to provide an incremental understanding of its empathic behavior,
e.g., by testing the chatbot on its level of emotional communication. On the
other hand, the system-level evaluation focuses on measuring the chatbot’s
overall perception of empathy, e.g., by conducting self-assessment empathy
tests [23].

In our work, we conduct a feature-level evaluation of ChatGPT’s performance in
showing parallel emotional responses, further explained in Sections III and
IV. Furthermore, we perform system-level evaluations by conducting four
standardized empathy tests and one autism test. Several studies have found
that individuals with autism may have difficulty with the cognitive component
of empathy, such as perspective taking and understanding others’ mental
states, while still being able to experience emotions and show affective
empathy, such as feeling concerned or compassion for others [25].

##  III Understanding and Expressing Emotions

To analyze ChatGPT’s ability to understand and generate emotions, our first
goal was to evaluate its proficiency in rephrasing neutral sentences to
express a particular emotion.

###  III-A Experimental Setup

For our analyses, we instructed ChatGPT to rephrase neutral sentences into six
emotional sentences of the following categories: joy, anger, fear, love,
sadness, and surprise. These emotions were selected following the basic
emotions from the Junto’s Wheel of Emotions [26] for consistency with the
experiments described in Section IV, where we trained a classifier using the
CARER dataset [27], which contains these categories. To check ChatGPT’s
ability to handle neutral sentences from different domains, we used 10
sentences from self-produced everyday sentences, Wikipedia, and Amendments to
the United States Constitution. We instructed ChatGPT to rephrase each neutral
sentence six times—each time with a different emotion category—resulting in a
total of 60 emotional sentences. Figure 1 illustrates how ChatGPT rephrased
the sentence ‘We are celebrating my grandmother’s 80th birthday today.” to
express joy and anger.

Original prompt: “We are celebrating my grandmother’s 80th birthday today.”
Rephrased as joy: “Hooray! Today is my grandmother’s 80th birthday, and we are
absolutely thrilled to be celebrating!” Rephrased as anger: “I can’t believe
we’re supposed to be celebrating my grandmother’s 80th birthday today - I am
absolutely furious about it! There’s so much to do, so many people to deal
with, and it’s just one more thing that I have to manage.” Figure 1: ChatGPT’s
Rephrasing of a Neutral Prompt into Joy and Anger.

###  III-B Experiment and Results

To evaluate whether the prompts generated by ChatGPT match the intended
emotion category, we asked three people to label each of the 60 ChatGPT-
produced prompts with the most suitable emotion category out of the six
categories provided. Based on the human labels, we produced the reference
emotion categories using a majority voting as follows: If two annotators
agreed on the same emotion for one prompt, we took this emotion as the final
emotion category. In case our three annotators assigned completely different
emotion categories to one prompt, they discussed their decisions until they
agreed on one emotion category.

Agreement | Percentage
---|---
all annotators agree | 71.7%
two annotators agree | 26,6%
all annotators disagree | 1.7%
TABLE I:  Annotator Agreement for the 6 Emotion Categories.

The annotator agreement on our six emotion categories is listed in Table I. A
complete agreement occurred in 71.7% of the cases. In 26.6% of the prompts,
exactly two annotators assigned the same emotion category. In only 1.7% of the
prompts, three different emotion categories were assigned. In the next step,
we compared the results from the manual annotation of the prompt generated by
ChatGPT to the intended emotion category.

Figure 2: Accuracy of Understanding and Expressing Emotions.

The results of the experiment are illustrated in Figure 2. The green line
indicates the average classification accuracy over our six emotion categories.
We see that when it comes to expressing emotions, ChatGPT can express the
desired emotion with an accuracy of 91.7%. The reference was labeled
differently in only 5 out of the 60 generated sentences. Anger, fear and love
were produced with an accuracy of 100%, surprise with 91%, joy with 83%, and
sadness with 80%.

##  IV Parallel Emotional Responding

Empathic behavior in a conversation consists of two components [28]: First the
emotion category of the conversational partner is identified (cognitive
empathy). After that, a response is generated that addresses the emotion
category of the conversational partner (affective empathy). A parallel
emotional response is defined as an emotional response where one individual
shows the same emotion as another individual in response to a particular
situation or stimulus [12]. This response can be observed when individuals
share a similar emotional experience, leading to the concurrent manifestation
of the same emotion in both individuals [12]. For instance, feeling joy when
another person expresses joy is a common example of a parallel emotional
response. In the following study, we focus on the analysis of ChatGPT’s
ability to generate parallel emotional responses.

###  IV-A Experimental Setup

For these experiments, we used 20.3k initial prompts from conversations in the
EmpatheticDialogues dataset from Facebook
Research111https://github.com/facebookresearch/EmpatheticDialogues [29]—named
as Speaker prompts—to trigger ChatGPT with initial emotional prompts and
generate a response, which we then classified and evaluated. Figure 3
demonstrates an initial joyful prompt from EmpatheticDialogues and ChatGPT’s
parallel emotional response.

Speaker: “I got a raise at my job today and I’ve only been there three months!
I was so shocked I couldn’t believe it.” ChatGPT: “Congratulations on your
raise! That is an amazing accomplishment, especially considering that you have
only been at your job for three months. You must be doing an excellent job,
and your hard work is being recognized. Keep up the good work, and enjoy the
benefits of your pay increase.” Figure 3: Joyful Prompt from
EmpatheticDialogues and ChatGPT’s Parallel Emotional Response.

Since it was impossible to manually classify the large number of 20.3k
ChatGPT-generated responses into emotion categories, we used an emotion
classification system, based on the Bidirectional Encoder Representations from
Transformers (BERT) [30]. The system was fine-tuned on the 16k training and 2k
validation sentences of the CARER
dataset222https://huggingface.co/datasets/dair-ai/emotion [27]. CARER consists
of tweets labeled with our 6 emotion categories love, joy, anger, fear,
sadness, and surprise. Our fine-tuning reached convergence after 8 epochs,
resulting in a performance of 63% when applied to our 60 manually annotated
prompts used in Section III-B. Analyzing this system demonstrated that the
category love reduced the system performance by 15% absolute. Therefore, we
removed the prompts labeled with love in the CARER training and validation
sets and re-trained the system resulting in an accuracy of 78%. To contribute
to the improvement of empathic chatbots, we share
ChatGPTsEmpatheticDialogues—our corpus, which consists of EmpatheticDialogues’
initial prompts, ChatGPT’s responses, and the corresponding emotion
categories—with the research community333https://github.com/iu-ai-
research/ChatGPTsEmpatheticDialogues.

TABLE II: Emotional Responses by ChatGPT (normalized)

###  IV-B Experiment and Results

In our analysis, we focused on parallel emotional responses, i.e., to what
percentage ChatGPT reacts with the same emotional category as in the initial
prompts. Table II illustrates the distribution of emotional responses to each
emotion category based on our classification system’s output. The results
indicate that the emotional responses are strongly biased towards replying
with joy. In 96.1% of the cases, ChatGPT’s emotional response to a prompt
categorized as joy was joy. Moreover, for anger and surprise, we observe even
more responses categorized as joy than for the original emotion category.
About half of the initial prompts with sadness and fear are answered with the
same emotion category. Overall, in 70.7% (20,237 responses), ChatGPT responds
with the same emotion category as the initial prompt.

Figure 4: Distribution of ChatGPT’s Emotional Responses.

Figure 4 visualizes the distribution of all emotion categories produced by
ChatGPT. With 40% of responses categorized as joy, we observe a strong
tendency of ChatGPT to reply in a positive way.

##  V How Empathic is ChatGPT’s Personality?

To learn more about ChatGPT’s empathic capabilities, we conducted system-level
evaluations using psychologically acknowledged questionnaires to evaluate
ChatGPT’s empathy level in different aspects.

We used five standardized questionnaires to assess empathy: Interpersonal
Reactivity Index, Empathy Quotient, Toronto Empathy Questionnaire, Perth
Empathy Scale, and Autism Spectrum Quotient. In this section, we will describe
the content of each questionnaire and how we used it to gather further
insights about ChatGPT’s empathic capabilities.

To get ChatGPT’s answer to each question in the questionnaires, we used the
questions as initial prompts and then evaluated ChatGPT’s response with
respect to the emotion category as follows: For each of ChatGPT’s answers, we
had our three annotators decide which possible answer in the questionnaire it
matched using the same rules for majority voting as described in Section
III-B. We had to perform this procedure as ChatGPT did not directly provide us
with the responses expected in the questionnaire, such as strongly agree or
strongly disagree.

As an alternative to manually matching ChatGPT’s answers and the answers in
the questionnaire, we tried the following sentence vector-based approach: We
converted ChatGPT’s answer and the answers in the questionnaire to word
embeddings using Sentence-BERT444https://github.com/UKPLab/sentence-
transformers [31], and then mapped ChatGPT’s answer to the answer with the
smallest distance in the semantic vector space. However, we had to discard
this approach as it did not perform well, with an accuracy of 38.5%, i.e.,
only 70 of 182 tested answers could be mapped correctly.

In the following paragraphs, we will present the results of the
questionnaires.

###  V-A Interpersonal Reactivity Index

The Interpersonal Reactivity Index (IRI) is a widely utilized self-report
measure for assessing empathy in individuals [32]. We chose to have ChatGPT
conduct the IRI since it has been used in various research and clinical
settings to understand empathy better and develop interventions to improve
empathy skills, e.g. [33] or [34]. In addition, the questionnaire covers the
categories of fantasy, personal distress, perspective taking, and empathic
concern.

####  V-A1 Experimental Setup

The IRI comprises 28 questions that evaluate the following four components of
empathy measured in subscales which are part of the overall scale of the
questionnaire: perspective taking, empathic concern, personal distress, and
fantasy. Perspective taking refers to an individual’s ability to understand
the perspectives of others, empathic concern to the ability to feel compassion
and concern for others, personal distress to the tendency to experience
anxiety or discomfort in response to others’ negative experiences, and fantasy
to the tendency to imagine oneself in fictional situations. The level of
agreement with each statement is rated on a 5-point Likert scale ranging from
does not describe me well to describes me very well. The scores for each
subscale of the IRI are obtained by summing the responses to the questions
that belong to that subscale, resulting in a score that ranges from 0 to 28.
Higher scores on the perspective taking and empathic concern subscales
indicate greater empathy, while lower scores on the personal distress subscale
suggest better emotional regulation.

####  V-A2 Experiment and Results

Figure 5 visualizes ChatGPT’s performance on the four IRI subscales and the
mean performance of males and females on [32]. For a comparison with the other
questionnaires, the absolute scores are not displayed in the figure, but the
percentage achieved compared to the possible total score of 28. Comparing
ChatGPT’s absolute score for fantasy reveals interesting results: while the
score of 17 is significantly higher than the mean score of healthy males
(15.73, $SD=5.6$, $t(578)=5.46$, $p<.001$) it is significantly lower than the
score of healthy females (18.75, $SD=5.17$, $t(581)=-8.17$, $p<.001$). For
perspective taking the absolute score of 16 is significantly lower than the
mean score of males (16.78, $SD=4.72$, $t(578)=3.98$, $p<.001$) and females
(17.96, $SD=4.85$, $t(581)=-9.75$, $p<.001$) demonstrating that ChatGPT has
lower abilities to take the perspective of others and understand their
feelings than healthy humans. ChatGPT’s score for empathic concern (11) is
much lower than the mean scores of males (19.04, $SD=4.21$, $t(578)=-45.95$,
$p<.001$) and females (21.67, $SD=3.83$, $t(581)=-67.21$, $p<.001$) indicating
that ChatGPT has a significantly lower level of emotional response to others.
Finally, also the absolute score for personal distress of 9 is significantly
lower than the mean of healthy males (9.46, $SD=4.55$, $t(578)=-2.43$,
$p<.05$) and females (12.28, $SD=5.01$, $t(581)=-15.79$, $p<.001$). Taken
together, in almost all dimensions of the IRI ChatGPT performs significantly
worse than healthy humans.

Figure 5: ChatGPT’s IRI Results Compared to Males/Females.

###  V-B Empathy Quotient

The Empathy Quotient (EQ) is a self-reported questionnaire that has been
specifically designed to assess an individual’s ability to comprehend and
respond to others’ emotions [25]. We chose to have ChatGPT conduct the EQ
since the questionnaire was conducted on two groups: one group with Asperger
syndrome / high-functioning autism (AS/HFA) and another group of healthy
humans. In the evaluation study, a clear threshold was identified to differ
between the two groups, which allows us to assign the score we achieve with
ChatGPT to one of those groups.

####  V-B1 Experimental Setup

The EQ comprises 60 questions, each with four possible responses: strongly
agree, agree, disagree, and strongly disagree. The questions cover a variety
of topics related to social interaction, emotional recognition, and
communication. Scores on the EQ can range from 0 to 80, with higher scores
indicating a greater capacity for empathy.

Figure 6: ChatGPT’s EQ Results Compared to Males/Females.

####  V-B2 Experiment and Results

Figure 6 displays ChatGPT’s EQ performance (green line) compares to healthy
males’ and females’ average EQ scores. The bars indicate the standard
deviations. The mean scores reported by [25] are 41.8 ($SD=11.2$) for healthy
males and 47.2 ($SD=10.2$) for healthy females. According to [25], more than
80% of people diagnosed with AS/HFA obtained a score below 30. Thus, with an
EQ score of 34, ChatGPT performs significantly lower than males
($t(70)=-5.62$, $p<.001$) and females ($t(125)=-14.53$, $p<.001$). Moreover,
it scores higher than an average person with AS/HFA.

###  V-C Toronto Empathy Questionnaire

Another tool to measure self-reported empathy is the Toronto Empathy
Questionnaire (TEQ) [35]. We decided to use the TEQ, as the questionnaire
tries to establish a general agreement between previous questionnaires such as
the IRI, the Autism Quotient, and many more.

####  V-C1 Experimental Setup

The TEQ consists of 16 questions that measure different components of empathy,
including affective empathy (the ability to experience and understand the
emotions of others) and cognitive empathy (the ability to understand the
thoughts and perspectives of others). The TEQ also includes questions
assessing an individual’s tendency to take another person’s perspective and
willingness to help others. Scores on the TEQ can range from 0 to 64, with
higher scores indicating a greater capacity for empathy.

Figure 7: ChatGPT’s TEQ Results Compared to Males/Females.

####  V-C2 Experiment and Results

Figure 7 illustrates how ChatGPT compares to the scores from a validation
study, differentiated by males and females. The bars indicate the standard
deviations. ChatGPT achieved a total of 41. In the validation study with 65
students from the University of Toronto, students achieved a mean score of
46.96 (SD = 7.47) [35]. Also, in the TEQ, females scored higher than males
(48.33 vs. 43.63). ChatGPT’s score of 41 is only slightly lower than the score
of males ($t(18)=-1.45$, $p=.165$) and significantly lower than the score of
females ($t(45)=-7.20$, $p<.001$).

###  V-D Perth Empathy Scale

The Perth Empathy Scale (PES) is a recently published self-report
questionnaire consisting of 20 questions to assess empathy in adults and
adolescents [36]. In addition to other existing scales, it covers the
cognitive and affective components of empathy and the positive and negative
dimensions of affective empathy. We selected this questionnaire as the
splitting into positive and negative empathy can be seen as additional
information not covered by the other analyzed questionnaires.

####  V-D1 Experimental Setup

Each category of the PES includes five sentences that cover 10 emotions,
including the five of the basic emotions described by [37] (i.e., happiness,
sadness, anger, scared, disgust), the self-conscious emotions of embarrassment
and pride, and the positive emotions of amusement, calmness, and enthusiasm.
Respondents rate their level of agreement or disagreement with each statement
on a 5-point Likert scale ranging from never to always. The PES yields a
general empathy score from 0 to 100, calculated by adding the scores from the
four scales. The questions for affective empathy ask if the emotions belong to
someone else, while for cognitive empathy, the questions ask about someone
else’s feelings, indicating a self-other distinction. The higher the total
score, the higher the level of empathy of an individual.

####  V-D2 Experiment and Results

ChatGPT scored 40 out of 100 possible points on the PES, which is
significantly below the score of healthy individuals (males: 64.1
($SD=10.92$), $t(187)=-30.26$, $p<.001$, females: 66.9 ($SD=11.27$),
$t(450)=-50.63$, $p<.001$) [36]. Figure 8 shows how the score distributes
amongst the subscales of the PES and how ChatGPT compares to the mean scores
of healthy humans. As a validation study showed that positive and negative
cognitive empathy are highly correlated, both values are summed up in the
figure. As observed in Section IV-B, we detect a higher tendency toward
positive empathy than toward negative empathy.

Figure 8: ChatGPT’s PES Results Compared to Males/Females.

###  V-E Autism Spectrum Quotient

Several studies have found that individuals with autism may have difficulties
with the cognitive component of empathy, such as perspective taking and
understanding others’ mental states, while still being able to experience
emotions and show affective empathy, such as feeling concerned or compassion
for others [25]. Therefore, we decided to additionally analyze the Autism
Spectrum Quotient (AQ)—a questionnaire that measures the autistic traits in
individuals who may or may not have a formal diagnosis of autism [38]. The AQ
has been shown to be inversely correlated with the EQ [25].

####  V-E1 Experimental Setup

On the AQ, respondents rate their level of agreement with each statement on a
4-point Likert scale ranging from definitely agree to definitely disagree. The
AQ measures five different skills: communication (verbal and nonverbal
communication), social (social interaction and understanding social cues),
imagination (imaginative and flexible thinking), local details (tendency to
focus on details and a preference for structured and predictable
environments), and attention switching (changing focus from one topic to
another).

The value of the AQ score ranges from 0 to 50, with 10 points for each skill.
In contrast to the previously presented questionnaires, a higher score on the
AQ refers to a lower level of empathy.

####  V-E2 Experiment and Results

As shown in Figure 9, in our experiment, ChatGPT achieved a total score of 19,
which is only slightly higher than the mean scores of healthy males (17.8,
$SD=6.8$, $t(75)=1.54$, $p=.128$) but significantly higher than the mean
scores of healthy females (15.4, $SD=5.7$, $t(97)=6.25$, $p<.001$). Moreover,
people diagnosed with AS/HFA show a mean score of 35.8 [38]. As for the
previous questionnaires, ChatGPT’s scores are worse than average healthy
humans but are still far away from the mean score a person diagnosed with
AS/HFA would achieve.

Figure 9: ChatGPT’s AQ Compared to Males/Females/AS/HFA.

Figure 10 illustrates how the scores are distributed amongst the respective
skills compared to the average male and female scores. In the figure, the
achieved scores are shown as a percentage in relation to the maximum possible
score.

Figure 10: ChatGPT’s AQ Results Compared to Males/Females. | Total | Mean (SD) |  |  |
---|---|---|---|---|---
| ChatGPT | Males | Females | Range | $\Delta_{ChatGPT:Males}$ | $\Delta_{ChatGPT:Females}$
IRI – Fantasy | 17 | 15.73 (5.60) | 18.75 (5.17) | 0-28 |  8% higher | 10% higher
– Perspective Taking | 16 | 16.78 (4.72) | 17.96 (4.85) | 0-28 |  5% lower | 12% lower
– Empathic Concern | 11 | 19.04 (4.21) | 21.67 (3.83) | 0-28 | 73% lower | 97% lower
– Personal Distress | 9 | 9.46 (4.55) | 12.28 (5.01) | 0-28 |  5% lower | 36% lower
EQ | 34 | 41.8 (11.7) | 47.2 (10.2) | 0-80 | 23% lower | 39% lower
TEQ | 41 | 43.63 (7.93) | 48.33 (6.90) | 0-64 |  6% lower | 18% lower
PES – General Cognitive Empathy | 22 | 37.6 (7.13) | 38.4(7.06) | 0-50 | 71% lower | 75% lower
– Positive Affective Empathy | 11 | 15.4 (4.05) | 16.1 (3.94) | 0-25 | 40% lower | 46% lower
– Negative Affective Empathy | 7 | 11.1 (3.39) | 12.4 (3.71) | 0-25 | 59% lower | 77% lower
– General Empathy | 40 | 64.1 (10.92) | 66.9 (11.27) | 0-100 | 60% lower | 67% lower
AQ | 19 | 17.8 (6.8) | 15.4 (5.7) | 0-50 |  7% higher | 23% higher
TABLE III: ChatGPT’s Empathy Scores Compared to the Mean Score of Males and
Females. (Note: In contrast to the scores of the other questionnaires, a
higher AQ score refers to a lower level of empathy.)

The scores for ChatGPT show that, especially for social skills, the score is
higher than for healthy adults, which is in line with the score for empathic
concern of the IRI. This can be seen as an indicator that ChatGPT has
difficulties fully connecting to other people’s feelings and feeling concerned
or compassionate for them. Moreover, imagination skills are worse than the
average for healthy humans. If it comes to attention switching, ChatGPT
performs considerably well, while the skill to focus on details and the
communicative skills of ChatGPT is quite similar to those of healthy humans.

##  VI Conclusion and Future Work

In the studies presented, we investigated the empathic capabilities of
ChatGPT. In our first study, we demonstrated that ChatGPT is able to rephrase
a sentence to express a particular emotion with an accuracy of 91.7%. This
shows that ChatGPT has the potential to be used as a tool for expressing
emotions on demand which can help in the interaction with humans—be it in a
learning environment or when being used as a source of everyday companionship.
In a second study, we additionally demonstrated that ChatGPT can generate
parallel emotional responses with 70.7% accuracy, meaning that it is able to
respond with the same emotion as the initial prompt in many cases.
Furthermore, our results show that ChatGPT has a strong tendency to reply with
joy. In our last study, we used five questionnaires to test the empathic
capabilities of ChatGPT. The questionnaires indicated that ChatGPT is able to
interpret the emotions of others and take their perspective but still has some
difficulties showing a higher level of empathy compared to healthy humans. All
scores from the questionnaires in comparison to healthy males and females are
summarized in Table III. While [9] concluded that GPT-3 shows a significant
lack of empathy based on the psychopathy section of the SD-3 [10]
questionnaire, in our empathy-focused studies we demonstrated that ChatGPT
expresses empathy in several aspects.

With our research, we show a possible way to proceed with analyzing chatbots
in the future. Further research should focus on developing more sophisticated
models that can more accurately grasp the emotional context of a conversation,
as well as on the development of methods to measure the emotional capabilities
of a chatbot. In addition, studies should be conducted to explore how ChatGPT
can be used as a tool to support people more compassionately. Finally, it is
important to consider the ethical implications of using chatbots such as
ChatGPT. This is particularly important because they often interact with
people who may not be aware that they are interacting with a computer program.
Developing methods for assessing the ethical implications of using chatbots
can help ensure that they are used ethically and that potential harm is
minimized.

## Ethical Impact Statement

Individuals were asked to label the text data we collected for our data
annotation. The participants who supported us were not dependent on the
authors and participated voluntarily and free of charge. There was no conflict
of interest between the supporters and the authors. For privacy reasons, the
names of the supporters are not disclosed.

The collected corpus is made freely available to the community. The collected
text data of the corpus are extracts from the EmpatheticDialogues dataset from
Facebook Research [29] and produced by ChatGPT. Usually, ChatGPT produces text
appropriate for the general public, but it cannot be ruled out that the
content is not suitable for everyone. The text data contains emotional
sentences in various forms. But this is the essence of a corpus that can be
used to evaluate a chatbot’s output realistically.

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# Primary disks and their observational appearance in collapsing magnetic
rotating protostellar clouds

Natalya S. Kargaltseva1,2, Sergey A. Khaibrakhmanov1,2, Alexander E.
Dudorov2,1, Andrey G. Zhilkin3
1Ural Federal University, 51 Lenin st., Ekaterinburg 620000, Russia
2Chelyabinsk State University, 129 Br. Kashirinykh st., Chelyabinsk 454001,
Russia
3Institute of Astronomy of the Russian Academy of Sciences, 48 Pyatnitskaya
st., Moscow 119017, Russia E-mail<EMAIL_ADDRESS><EMAIL_ADDRESS>
(Accepted 08.07.2021)

###### Abstract

The collapse of the magnetic rotating protostellar cloud with mass of
$10\,M_{\odot}$ is numerically studied. The initial ratios of the thermal,
magnetic, and rotational energies of the cloud to the modulus of its
gravitational energy are 0.3, 0.2 and 0.01, respectively. The emphasis is on
the evolution and properties of the quasi-magnetostatic primary disk formed at
the isothermal stage of the collapse. Simulations show that the primary disk
size and mass increase during evolution from $1500$ au to $7400$ au and from
$0.3\,M_{\odot}$ to $5.2\,M_{\odot}$, respectively. Magnetic field is quasi-
radial in the cloud envelope and quasi-uniform within the primary disk. A
toroidal magnetic field is generated behind the front of the fast shock MHD
wave propagating from the primary disk boundary and in the region of the
outflow formed near the first hydrostatic core. The hierarchical structure of
collapsing protostellar clouds can be revealed in observations in terms of the
magnetic field geometry and the angular momentum distribution.

###### keywords:

magnetic fields, magnetohydrodynamics (MHD), numerical simulations, star
formation, interstellar medium

††pubyear: 2021††pagerange: Primary disks and their observational appearance
in collapsing magnetic rotating protostellar clouds–LABEL:lastpage

##  1 Introduction

The current star formation occurs in magnetic rotating cores of molecular
clouds, i.e., protostellar clouds (hereafter, PSCs). During the gravitational
collapse of the PSC, a protostar is formed at its center, which is observed as
an infrared source (see Zhang et al., 2020). The young protostar immersed in
an extended envelope is observed in the submillimeter range as a Class 0 young
stellar object (hereafter, YSO). Characteristic features of Class 0 YSOs are
outflows (Andre et al., 1993). In Class 0 YSOs, flattened envelopes with radii
of$200-10000$ au are observed, as well as small probably Keplerian disks with
radius of $5-50$ au (Ohashi et al., 1997; Wiseman et al., 2001). In YSO
envelopes, a large-scale magnetic field with hourglass geometry is detected;
within disks, a pinch magnetic field and indications of a toroidal magnetic
field are observed (see Lee et al., 2019). The angular momentum distribution
changes in going from the disk to the envelope (Caselli et al., 2002).

The first simulations of the PSC collapse showed that the collapse is
inhomogeneous with the formation of the first hydrostatic core at the cloud
center (Larson, 1969). During the collapse, the magnetic rotating PSC takes a
shape oblate along magnetic field lines and/or the rotation axis (Scott &
Black, 1980). The PSC magnetic flux evolution and properties of formed stars
are substantially controlled by ionization, recombination, and diffusion MHD
effects, in particular, ambipolar diffusion (Dudorov & Sazonov, 1987).

The basic questions in the star formation theory are the problems of the
angular momentum and catastrophic magnetic braking (Scott & Black, 1980;
Dudorov & Sazonov, 1982). The modern numerical simulations are mostly devoted
to the accretion stage of the solar-mass PSC collapse (see Hennebelle &
Fromang, 2008; Zhao et al., 2020). To solve the angular momentum problem, it
is important to comprehensively study initial PSC collapse stages, when
magnetic braking is the most efficient.

Previously, Khaibrakhmanov et al. (2021) studied the isothermal collapse of
magnetic PSCs with masses of $1$ and $10\,M_{\odot}$. Simulations showed that
a hierarchical PSC structure is formed during isothermal collapse, which
consists of a geometrically thick and optically thin envelope, with a
geometrically and optically thin quiasi-magnetostatic primary disk (hereafter
PD) inside.

The PD boundary is characterized by a sharp jump in velocity profiles, when
almost free fall of the gas from the cloud envelope to its center transforms
to slow almost radial motion. At the PD boundary, a fast shock
magnetohydrodynamic (hereafter MHD) wave is formed, which moves to the cloud
periphery.

In the present study, the approach of Khaibrakhmanov et al. (2021) is
developed. The collapse of the magnetic rotating PSC with mass of $M_{\odot}$
is numerically simulated taking into account the formation of the first core.
The PD evolution is studied, its mass, size, angular momentum, magnetic flux,
and lifetime are determined. Possible observational manifestations of PDs are
discussed.

##  2 Problem statement and numerical method

A homogeneous spherically symmetric rotating PSC with mass of 10 $M_{\odot}$
and a temperature of 20 K, in a uniform magnetic field is considered. The
initial cloud density is $4\cdot 10^{4}$ cm-3, the initial cloud radius is
$0.1$ pc. The main parameters determining the collapse dynamics are the ratios
of the thermal $\varepsilon_{\rm t}$, magnetic $\varepsilon_{\rm m}$ and
rotational $\varepsilon_{\rm w}$ energies to the modulus of its gravitational
energy. In this paper, we consider the simulation with $\varepsilon_{\rm
t}=0.3$, $\varepsilon_{\rm m}=0.2$ and $\varepsilon_{\rm w}=0.01$.

The PSC collapse is studied using gravitational MHD equations. Numerical
simulation is performed using the two-dimensional MHD code ‘Enlil’ (Dudorov et
al., 1999; Zhilkin et al., 2009). The PSC thermal evolution is simulated using
the gas law with a density-dependent effective adiabatic index, $\gamma_{\rm
eff}$ (Masunaga & Inutsuka, 2000). For the isothermal collapse $\gamma_{\rm
eff}=1.001$ is taken. At the density $\rho\geq 10^{-13}$ g cm-3, when the
first hydrostatic core is formed (Larson, 1969), $\gamma_{\rm eff}=5/3$. This
approach allows us to model the PSC collapse taking into account the first
core formation.

##  3 Evolution of the primary disk during the protostellar cloud collapse

The performed simulations confirm the conclusions by Khaibrakhmanov et al.
(2021). At the isothermal collapse stage, the cloud gains a hierarchical
structure: the envelope takes a shape oblate along magnetic field lines and
the rotation axis; a quasi-magnetostatic PD is formed within it. Let us
consider the general picture of the PD evolution with the emphasis on the
angular momentum distribution.

Figure 1 shows the quarter of the central part of the collapsing PSC at
different time points. The time $t$ is measured in the units of the
characteristic collapse time taking into account the effect of electromagnetic
and centrifugal forces: $t_{{\rm fmw}}=t_{{\rm ff}}(1-\varepsilon_{\rm
m}-\varepsilon_{\rm w})^{-1/2}$, where $t_{{\rm ff}}$ is the free fall time
Dudorov & Sazonov (1982). At the PD formation time, $t=0.9081$ $t_{{\rm fmw}}$
(Fig. 1a), its radius is $R_{\rm pd}\approx 0.07\,R_{0}\approx 1500$ au, and
the ratio of its maximum half-thickness to the radius is $Z_{\rm pd}/R_{{\rm
pd}}=0.039$. At the time point $t=0.9268$ $t_{{\rm fmw}}$ (Fig. 1b), the PD
radius is $\approx 0.22\,R_{0}\approx 4500$ au. At the PD boundary, a fast MHD
shock wave is formed, which propagates into the cloud envelope (see
Khaibrakhmanov et al., 2021). The first core is formed at time $t=0.9645$
$t_{{\rm fmw}}$ (Fig. 1c). Near the core, in the region $r<0.04\,R_{0}\approx
800$ au, the PD thickness abruptly decreases, i.e., quasi-magnetostatic
equilibrium is broken. Then, gas in this region begins to move from the cloud
core to the periphery in parallel to the rotation axis, i.e., the outflow is
formed (Fig. 1d). The PD radius continues to increase and by the time of
$t=0.9966$ $t_{{\rm fmw}}$ (Fig. 1f) reaches $R_{\rm pd}\approx
0.36\,R_{0}\approx 7400$ au. During evolution, the PD becomes geometrically
thinner $Z_{\rm pd}/R_{{\rm pd}}=0.028$, and the outflow region sizes
increase.

Figure 1: Distribution of the angular momentum (color filling), velocity field
(arrows), and magnetic field (white lines) near the primary disk at following
time moments: a) $t=0.9081$ $t_{\rm fmw}$; b) $t=0.9268$ $t_{\rm fmw}$; c)
$t=0.9645$ $t_{\rm fmw}$; d) $t=0.9762$ $t_{\rm fmw}$; e) $t=0.9896$ $t_{\rm
fmw}$; f) $t=0.996$ $t_{\rm fmw}$. The green line is the primary disk
boundary.

At the PD formation time (Fig. 1a) the magnetic field is quasi-radial,
$B_{r}\sim B_{z}$,in the envelope and quasi-uniform, $B_{r}\ll B_{z}$, within
the PD. Further the magnetic field takes a toroidal geometry,
$B_{\varphi}\sim(B_{r},B_{z})$, behind the front of the fast shock MHD wave
and in the outflow region.

After the PD formation, the specific angular momentum is accumulated at its
boundary (Fig. 1b), and it is further transferred from the PD boundary to the
PSC envelope by the fast shock MHD wave (Fig. 1c). The angular momentum of the
first core is removed by the outflow (Figs 1d–f).

An analysis of the simulation shows that the PD mass increases with time from
$0.3\,M_{\odot}$ to $5.2\,M_{\odot}$. On the contrary, the PSC envelope mass
decreases from the time of the PD formation. The total angular momentum of the
PSC decreases by 15 % relative to the initial value $J_{0}$.

##  4 Conclusions and discussion

The present paper is the development of the study by Khaibrakhmanov et al.
(2021), in which the isothermal stage of the PSC collapse was modelled. Two-
dimensional numerical MHD simulation of the collapse of the magnetic rotating
PSC with mass of 10 $M_{\odot}$ was performed taking into account the first
hydrostatic core formation.

The simulations show that the hierarchical structure of the PSC formed at the
isothermal collapse stage Khaibrakhmanov et al. (2021), is retained during
further evolution. The first hydrostatic core is formed at the center of the
quasi-magnetostatic PD. Near the core, $r<0.04\,R_{0}\approx 800$ au, the
quasi-magnetostatic equilibrium is broken, and, afterwards, outflow arises.
The PD size and mass increase from 1500 au to 7400 au and from 0.3 $M_{\odot}$
to 5.2 $M_{\odot}$, respectively. These values are close to characteristics of
observed Class 0 YSO envelopes (Ohashi et al., 1997; Wiseman et al., 2001).
Therefore, it can be assumed that the observed large-scale oblate envelopes of
Class 0 YSOs are PDs.

Our results confirm the conclusions by Khaibrakhmanov et al. (2021) on the PD
lifetime and magnetic field geometry in the collapsing PSC. The PD is a long-
living structure which continues to evolve after the first core formation. The
magnetic field geometry is different across the hierarchy. The magnetic field
is quasi-radial within the envelope; it is toroidal behind the front of the
fast MHD wave emerging from the PD soon after its formation and in the outflow
region; the magnetic field is quasi-uniform within the PD. The PD plays an
important role in the evolution of the specific angular momentum in the cloud.
The angular momentum is transferred by the fast shock MHD wave travelling from
the PD boundary to the cloud envelope, as well as by the outflow formed near
the first core. The total angular momentum of the cloud, decreased by 15 %
relative to the initial value by the time $t=0.9645$ $t_{{\rm fmw}}$ $=0.1936$
Myrs, when a typical hierarchical PSC structure with outflows.

Based on our results, it can be assumed that the hierarchical structure of
collapsing PSCs can be revealed in observations in terms of the distribution
of the magnetic field geometry and the angular momentum. To determine the
magnetic field geometry at various hierarchy levels, polarization maps of
Class 0 YSOs should be constructed in the submillimeter range with high
spatial resolution.

The further study will be directed to the determination of the mass, size,
total angular momentum, and magnetic flux of the PD, as well as of the first
hydrostatic core during the collapse of magnetic rotating PSCs with various
initial cloud parameters and taking into account magnetic field diffusion.

Acknowledgments This study was supported by the Russian Science Foundation,
project no. 19-72-10012. The authors are grateful to reviewer E.O. Vasil’ev
for helpful comments.

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