11institutetext: Michigan State University, USA 22institutetext: Rochester
Institute of Technology, USA
22email<EMAIL_ADDRESS>
# A Linguistic Comparison between Human and ChatGPT-Generated Conversations

Morgan Sandler 11 Hyesun Choung 11 Arun Ross 11 Prabu David 22

###### Abstract

This study explores linguistic differences between human and LLM-generated
dialogues, using 19.5K dialogues generated by ChatGPT-3.5 as a companion to
the EmpathicDialogues dataset. The research employs Linguistic Inquiry and
Word Count (LIWC) analysis, comparing ChatGPT-generated conversations with
human conversations across 118 linguistic categories. Results show greater
variability and authenticity in human dialogues, but ChatGPT excels in
categories such as social processes, analytical style, cognition, attentional
focus, and positive emotional tone, reinforcing recent findings of LLMs being
“more human than human.” However, no significant difference was found in
positive or negative affect between ChatGPT and human dialogues. Classifier
analysis of dialogue embeddings indicates implicit coding of the valence of
affect despite no explicit mention of affect in the conversations. The
research also contributes a novel, companion ChatGPT-generated dataset of
conversations between two independent chatbots, which were designed to
replicate a corpus of human conversations available for open access and used
widely in AI research on language modeling. Our findings increase
understanding of ChatGPT’s linguistic capabilities and inform ongoing efforts
to distinguish between human and LLM-generated text, which is critical in
detecting AI-generated fakes, misinformation, and disinformation.

###### Keywords:

Social Computing Computational Linguistics LLMs.

##  1 Introduction

Words are the building blocks of human language, and the richness of human
language enables expressions of intricate thoughts and feelings. From the
early days of computing, endowing machines with such human language capability
has captured the imagination of technologists [1].

Until recently, those efforts to build chatbots with language proficiency have
come up short. But with the rapid advances in generative large language models
(LLM), AI applications such as ChatGPT and Bard are demonstrating
proficiencies, albeit in specific settings and domains [2, 9], signaling the
dawn of new possibilities and challenges in natural language processing and
artificial intelligence.

The success of LLMs at mimicking human language capabilities has been achieved
through machine learning algorithms that have ingested a vast amount of human
language data. Given the human origin of the training data, similarities in
language use can be expected between LLMs and humans, which heightens concerns
related to deepfakes, disinformation, misinformation, plagiarism, and
algorithmic bias [8]. To address this challenge, we examine potential
differences in linguistic features between human- and LLM-generated
conversations using computational linguistic analysis [3], a popular technique
used in psychology, communication, and related areas [13, 23, 10].

The analysis is built on computerized analysis of words and groupings of
words, which have been used successfully to characterize personality [6],
deception [17], authenticity [10], social status [11], and self-presentation
[12]. Researchers have found that careful analysis of words used in writing
and conversation can reveal underlying thoughts and emotions of the
communicator [3, 17, 4].

In a typical conversation, we focus more on content words that convey meaning,
emotions, and actions and less on grammatical or function words, such as
pronouns, conjunctions, and articles. However, these function words also are
associated with important communication and psychological dynamics [5].
Researchers believe that both content and function words play complementary
roles, with content words conveying what we say and function words conveying
how we say it [19, 4].

A widely used application for language analysis is Linguistic Inquiry and Word
Count (LIWC) [22], which comes with dictionaries that categorize words into
pronouns, prepositions, conjunctions, articles, positive and negative
emotions, words related to friends, family, and so on. These words are grouped
into higher-level categories (e.g., personal pronouns, impersonal pronouns,
affect, cognition, social processes, etc.) and used in formulas to compute
summary variables such as clout, authenticity, analytical thinking, and
emotional tone [3]. Linguistic categories generated by LIWC offer a rich
profile of language use, and, by extension, insights into the underlying
thoughts and emotions of the speaker or writer.

In this study, using LIWC analysis, we compared conversations between two
individuals and corresponding conversations between two ChatGPT-3.5 chatbots.
The corpus of human conversations was obtained from EmpathicDialogues, a
dataset with 25K dialogues associated with 32 emotions, which is available to
the public [20]. These dialogues were generated by conversations among 810
crowd-workers on Amazon Mechanical Turk (MTurk). Similar dialogues between two
ChatGPT chatbots were simulated by passing scenarios and emotions from the
EmpathicDialogues dataset through prompt engineering. Differences between
human conversations and ChatGPT conversations were examined for 118 linguistic
features generated by LIWC.

Among other linguistic categories, emotion is a compelling feature of language
and communication. To understand the encoding of emotion in ChatGPT, in
addition to the analysis of the linguistic features of affect, we examined the
implicit coding of affect valence in LLM embeddings using an emotion
classifier. The key contributions and findings of this study are summarized
below:

  1. 1.

A dataset consisting of 19.5K dialogues generated by two ChatGPT chatbots that
serve as a companion to the EmpathicDialogues dataset. This dataset, named
2GPTEmpathicDialogues, is a resource for communities interested in NLP and
language modeling.

  2. 2.

Comparisons of human conversations with ChatGPT conversations on 118
linguistic categories offered by LIWC.

  3. 3.

Findings suggest more variability and authenticity in human dialogues compared
to ChatGPT dialogues.

  4. 4.

On the other key linguistic categories such as social processes and behaviors,
analytical style, cognition, attentional focus, and positive emotional tone,
ChatGPT scored higher than humans, reinforcing recent findings [9], the LLMs
are “more human than human” in their language use.

  5. 5.

Linguistic features associated with positive and negative affect were not
different between ChatGPT and human conversations.

  6. 6.

Embeddings of ChatGPT and human conversations analyzed using a classifier
revealed implicit coding of positive and negative affect valence in the
embeddings even though there was no explicit mention of affect in the
dialogues.

In summary, we contribute a novel, ChatGPT-generated dataset and findings that
can be used to advance research in NLP and language modeling. Further, our
study offers a rigorous comparison of linguistic differences between human
dialogues and dialogues simulated between two independent ChatGPT-3.5
chatbots. Findings from this study contribute to the ongoing efforts to detect
differences between human and LLM-generated dialogues.

##  2 Generating Conversational Data

###  2.1 Human Conversations: EmpathicDialogues

Samples of human conversations used in this study were obtained from an open-
domain dataset offered by Rashkin et al. [20], which was developed to serve as
a resource for conversational models. The dataset consists of 25K distinct,
dyadic dialogues based on situations associated with 32 emotions and generated
by 810 individuals on Amazon Mechanical Turk (MTurk).

From the overall corpus of 25K conversations, the training dataset, which
consisted of 19,533 dialogues, was selected for this study. Each dialogue in
the dataset was generated by two conversational partners. The initiating
speaker in the dialogue chose from one of 32 emotions (e.g., afraid) and wrote
a scenario (e.g., hearing noises around the house at night) associated with
the emotion before sharing it with the other speaker in the conversation. The
speaker receiving the scenario was instructed to respond to the scenario but
was unaware of the emotion chosen by the initiating speaker. The speakers were
asked not to exceed six conversational turns after the initial exchange. On
average, each conversation lasted approximately four exchanges (M = 4.31).

###  2.2 ChatGPT Conversations: 2GPTEmpathicDialogues

To compare ChatGPT conversations with human conversations, we developed a
dyadic conversational system that replicated the design of human conversations
in EmpathicDialogues dataset. The system consisted of two independent
instances of ChatGPT that communicated through a Coordinator Program (see
Figure 1). The coordinator program, developed in Python, establishes dyadic
communication between two ChatGPT-3.5-Turbo instances using OpenAI’s public-
use API. It manages two separate API sessions, processing and relaying
messages between the instances, and handles API calls efficiently. The program
incorporates error handling and asynchronous programming to ensure smooth and
consistent communication. This setup enables seamless exchange of information
and overcomes challenges arising from network issues. The initiating ChatGPT
was prompted to begin the conversation with a scenario associated with an
emotion. To liken the design to the human conversations, the instance of
ChatGPT receiving the scenario was not made aware of the emotion associated
with the scenario.

Figure 1: Framework for generation and prompts used in creating the
2GPTEmpathicDialogues dataset. In this setup, two instances of the
ChatGPT-3.5-Turbo API engage in conversation with each other through a
coordinating program.

A sample human conversation and an equivalent ChatGPT conversation are shown
side by side in Table 1. Using seed prompts (see Figure 1), the two instances
of ChatGPT communicated by passing messages via a Coordinator Program. Each
dialogue lasted approximately three to four turns, and the dialogue was
captured in a CSV file for further analysis. On some conversation turns we
observed role confusion, which was addressed by further clarifying the prompt,
although even this was not sufficient due to the stochastic nature of LLMs,
which is addressed in the limitations. Further, the temperature parameter was
set at 0.5 and a maximum token limit was set at 250 to limit verbosity.
Despite this setting, the word count for ChatGPT dialogues (M = 300, SD =
25.6) was significantly $(\textit{p}<.001)$ longer in comparison to the human
dialogues (M = 58, SD = 120.5), which is addressed in the section on
limitations. ChatGPT dialogues were generated using ChatGPT-3.5-Turbo API and
it took about 48 hours to generate the 19.5K conversations.

Table 1: Excerpt of Human-Generated vs. ChatGPT-Generated Empathic Dialogues.
Note: There is some observed role confusion due to the stochastic nature of
LLMs, which is addressed in the limitations.

Initiating Scenario |  Emotion |  EmpathicDialogue |  2GPTEmpathicDialogue
---|---|---|---
I was furious when I found out a classmate was being cruel to my daughter. |  Furious |  Speaker1: I was extremely upset when I found out a classmate was being cruel to my daughter

Speaker2: Oh that is terrible! Kids are so mean! What did you do?

Speaker1: I spoke with the school counselor and told her the concerns of my
daughter’s that it will get worse

Speaker2: I hope that it only gets better. |  GPT1: I cannot believe what I just found out! I am absolutely furious right now!

GPT2: What happened? Why are you so angry?

GPT1: I just found out that my best friend has been spreading rumors about me
behind my back. It’s so hurtful and infuriating to think that someone I
trusted would do that to me. I feel so betrayed.

GPT2: I can understand why you’re feeling furious and hurt right now.
Discovering that someone you considered a close friend has been spreading
rumors about you is a significant breach of trust. It’s natural to feel
betrayed in this situation. Is there anything I can do to support you?

##  3 Analysis and Results

Analysis was conducted in three steps: (1) analyzing and coding the linguistic
features in the conversations, (2) statistical comparisons of the linguistic
features extracted by LIWC, (3) additional valence classification analysis
using the LLM embeddings that represent human- and ChatGPT-generated
dialogues.

###  3.1 LIWC Analysis

Dialogues from humans and ChatGPT were analyzed with LIWC-22 [3], which codes
and summarizes the linguistic features of dialogues into 118 categories. Each
category is a dictionary of curated words chosen through research for their
correlation to psychological constructs. The category counts were converted
into a percentage based on the total number of words analyzed. The linguistic
dimension scores for each ChatGPT conversation were matched with the scores
for the equivalent human conversation, and the differences between the two
conditions were examined. Summary statistics and statistical significance
tests for all 118 linguistic categories are presented in Appendix 2 available
on our Github link provided in Section 4. From the 118 categories, in this
paper, we focus only on a few higher-level categories associated with social
process, cognition, and emotion.

###  3.2 Comparison between Human and ChatGPT Dialogues

Differences in mean and variance for each linguistic feature were examined
using an independent sample t-test and Levene’s test for equality of variance.
To adjust for the number of significance tests, which can lead to higher
likelihood of type I error and false positives, a Bonferroni correction was
applied and statistical significance was set at $p<.001$.

Out of the 118 categories, 110 were significant at $p<.001$. The absence of
statistical significance was observed for a few linguistic features, such as
space within the perception category and health within the physical category.
Null effects were also observed for positive and negative emotions which are
examined in detail later in the paper. From among the categories that were
significantly different, we examined only those associated with social
processes, cognition, and emotion.

Along with mean differences, we also examined differences in variance for each
of the 118 categories using Levene’s test. As expected, the variance between
human- and ChatGPT-generated conversations was different at $p<.0001$ for all
118 linguistic categories. The lower variance in ChatGPT conversations can be
attributed to the temperature setting of 0.5 used in this study and to the
characteristics of the LLM that is trained to yield predictions that appeal to
a broad range of users.

Social Behaviors. Given the efforts to make chatbots social and the critical
role of language in conveying social information, we used the LIWC’s social
behavior category to examine ChatGPT’s linguistic features signifying social
behaviors, which is a composite measure of prosocial behaviors (e.g, caring,
helping), politeness (e.g., please, thank you), interpersonal conflict (e.g.,
fight, argue), moralization (e.g., good, bad) and communication (e.g., talk,
explain). While humans have real experience in such social behavior
categories, ChatGPT’s statistical models are tuned to predict these social
behaviors based only on context.

Linguistic features that characterize social behaviors were more prevalent in
ChatGPT conversations than human conversations (see Table 2 for summary
statistics). ChatGPT conversations scored higher on all subcategories,
including social behavior, prosocial behavior, politeness, and communication.
ChatGPT also scored less on interpersonal conflict. Effect size (.26 to .69)
was small to medium for all differences. In brief, ChatGPT conversations were
slightly more socially sensitive than human conversations.

Attentional Focus. According to best practices in communication, a good
conversational partner is an active listener interested in what others say.
This attentional focus directed toward others is captured by the clout index
in LIWC, which is derived from an analysis of personal pronouns. Over a series
of studies, it has been observed that people who focus on others use the
first-person singular pronoun (I) sparingly in comparison to the first-person
plural pronoun (we) and the second-person pronoun (you) [11].

Our findings show that ChatGPT (M = 64.79, SD = 23.91) conversations were
better ($\delta$ = 1.25, large effect) in attentional focus than human
conversations (M = 34.89, SD = 31.35) with more frequent use of you and we
pronouns and less frequent use of the I pronoun. ChatGPT demonstrates empathy
and interest in others through the strategic use of pronouns, which is a
useful insight considering its wide deployment as a help agent in various
contexts.

Authenticity. Authenticity is a direct and simple mode of communication that
leads to positive outcomes like likability, trust, and support. An authentic
communication style connects with the general public and is well-suited for
contemporary media, such as TV and social. Over the years, political
communication has become more authentic and less nuanced [10]. Psychologists
[21] and communication researchers [14] have used the authenticity index in
LIWC, and details about the components of the formula can be found in a paper
by Markowitz et al. [14]. Although the origin of this category can be traced
to research on deception [17], it is distinct from deception. For example, one
can lie and still be authentic, which is not uncommon among politicians [10].
In this paper, we examined if conversations generated by ChatGPT are as
authentic as human conversations. Authenticity is the only category in which
humans (M = 63.99, SD = 33.53) outperformed ChatGPT (M = 52.49, SD = 27.66,
$\delta$ = .42). The effect was small to medium, highlighting a weakness in
ChatGPT’s ability.

Analytical Thinking. While authenticity captures the linguistic features of
simple and direct communication, analytical thinking in LIWC captures the use
of “formal, logical, and hierarchical thinking” [18]. Analytical thinking is
associated with reasoning and is detected by aggregating small function words
that receive little attention. While the use of articles and prepositions are
indicative of higher analytical thinking, pronouns, conjunctions, negation,
and adverbs are associated with lower analytical thinking that is more
“narrative and personal” [18]. On the linguistic dimension of analytical
thinking, scores for ChatGPT (M = 20.19, SD = 16.30) dialogues were higher
than human dialogues (M = 17.04, SD = 18.64), $\delta$ = .19 (small effect).

Cognition. Cognition is closely related to analytical thinking and is a key
psychological construct that includes sub-categories such as dichotomous
thinking, cognitive processes, and memory words (e.g., remember, forget).
Other components of cognition include insight, and cause and effect. For
cognition, ChatGPT-generated dialogues (M = 17.68, SD = 4.39) scored higher
than human dialogues (M = 13.01, SD = 5.77), $\delta$ = 1.06 (large effect).

Emotion. Human language is equipped to capture and convey the range of
emotions that make up the human experience. LIWC has a number of metrics to
examine the emotions expressed via language, including positive and negative
affect. In this study, we focus on two higher-level LIWC dimensions, namely
emotional tone and affect. Emotional Tone is operationalized as a
psycholinguistic variable that includes affect (e.g., joy, sorrow) as well as
words associated with affect (e.g., birth, death). Affect, on the other hand,
is limited only to emotions. The emotional dimensions from LIWC have been used
to examine sentiments in personal diaries following 9-11 [7], and on social
media during Covid [16].

The emotional tone of ChatGPT dialogues was more positive (M = 65.66, SD =
37.75) than human dialogues (M = 54.37, SD = 40.10), $\delta$ = .30 (small
effect). The positive emotions scores for ChatGPT (M = 2.40, SD = 2.00) and
humans (M = 2.36, SD = 2.59) were not significantly different. Similarly, for
negative emotions, the difference between ChatGPT (M = 1.45, SD = 1.60) and
humans (M = 1.49, SD = 2.05) was not significantly different.

Table 2: Comparison of Linguistic Features Between Human and ChatGPT
Conversations. Note: df for the unequal variance t-tests varied from row to
row and ranged between 27-39K. $\delta$ = Glass’ Delta, used to compute power
(0.2 or lower = small effect, 0.5 = medium effect, 0.8 or higher = large).

| Human Conversation | ChatGPT Conversation |
---|---|---|---
Category | Mean | SD | Mean | SD | Statistics
Social Behaviors (e.g., said, love, care) | 2.76 | 2.83 | 4.42 | 2.39 | t = 62.6, $p<.001$, $\delta$ = .69
Prosocial Behaviors (e.g., care, help) | 0.72 | 1.38 | 1.75 | 1.45 | t = 71.9, $p<.001$, $\delta$ = .71
Politeness (e.g., thank, please) | 0.22 | 0.78 | 0.49 | 0.05 | t = 38.3, $p<.001$, $\delta$ = .46
Interpersonal conflict (e.g., fight, attack) | 0.20 | 0.75 | 0.11 | 0.35 | t = 15.5, $p<.001$, $\delta$ = .26
Communication (e.g., sad, tell, thank) | 0.87 | 1.65 | 1.28 | 1.09 | t = 29.2, $p<.001$, $\delta$ = .38
Attentional Focus | 34.89 | 31.15 | 64.79 | 23.91 | t = 105.4, $p<.001$, $\delta$ = 1.25
Authenticity | 63.99 | 33.54 | 52.49 | 27.66 | t = 37.0, $p<.001$, $\delta$ = .42
Analytical thinking | 17.04 | 18.64 | 20.19 | 16.30 | t = 17.8, $p<.001$, $\delta$ = .19
Cognition | 13.01 | 5.77 | 17.68 | 4.39 | t = 89.9, $p<.001$, $\delta$ = 1.06
Emotional Tone | 54.37 | 40.10 | 65.66 | 37.75 | t = 28.65, $p<.001$, $\delta$ = .30
Emotion Positive | 2.36 | 2.59 | 2.40 | 2.00 | t = 1.73, ns, $\delta$ = .02
Emotion Negative | 1.49 | 2.05 | 1.45 | 1.60 | t = 2.00, ns, $\delta$ = .02

###  3.3 Valence Classification of ChatGPT Embeddings

Next, we analyzed ChatGPT embeddings to determine if emotion cues are latently
present in them. To explore ChatGPT embeddings, we conducted two types of
analysis: (a) a binary-class valence classification experiment involving the
training and testing of three different classifiers, and a (b) Uniform
Manifold Approximation and Projection (UMAP) [15] for the visualization of
each dataset in three dimensions. The valence classification experiment
assesses a classifier’s ability to distinguish between positive and negative
valence emotion cues latent within the ChatGPT embeddings. The UMAP experiment
explores the spatial distribution of embeddings with respect to their valence
categories, providing insight into how the different underlying emotion
categories are clustered in high-dimensional space by their corresponding
valence value. To arrange the dialogues for a binary valence classification
problem, we group the underlying emotions into positive or negative valence
(see Table 3).

Table 3: Binary Valence Classification of Emotions: 16 Positive and 16
Negative Valence Emotions.

Positive Valence |  Negative Valence
---|---
anticipating, caring, confident, content, excited, faithful, grateful, hopeful, impressed, joyful, nostalgic, prepared, proud, sentimental, surprised, trusting |  afraid, angry, annoyed, anxious, apprehensive, ashamed, devastated, disappointed, disgusted, embarrassed, furious, guilty, jealous, lonely, sad, terrified

Then, we extracted embeddings for each conversation in the Human and ChatGPT
datasets and examined them using three classifiers: Random Forest classifier,
Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). We used
OpenAI’s publicly available text-embedding-ada-002 model for extracting all
dialogues embeddings for both the human and ChatGPT-generated datasets. Then
we employed a stratified 5-fold cross-validation to ensure a balanced
representation of valence categories across the folds, and a grid search to
determine the hyperparameters for each model. Given that each dataset
comprises 19,533 samples, in each fold of our stratified cross-validation
process, we allocated 15,626 samples for training and 3,907 for testing, and
ensured an approximately equal representation of each underlying emotion
category between train and test sets. For the Random Forest classifier, we
evaluated 50, 100, and 200 estimators. In the case of the SVM, we examined
values of ‘C’ at 0.1, 1, and 10, and $\gamma$ at 0.001, 0.01, and 0.1. For the
MLP model, we tested a single-layer (100 nodes), triple-layer (300, 200, 100
nodes), and dual-layer (150, 150 nodes) configuration, with maximum iterations
set at either 300 or 500. Table 4 shows the average weighted F1-score across
all folds for each classifier. We observed that the SVM exhibited strong
performance for both datasets, achieving an average weighted F1-score of 90.0%
on the human-generated dataset and 95.3% on the ChatGPT-generated dataset.
Interestingly, the higher F1-score from the ChatGPT-generated dataset
classifier suggests that the language patterns used by ChatGPT may be more
consistent or distinct in expressing different valences compared to human-
generated text. This could be due to the structured nature of LLM-generated
language, which might adhere more closely to identifiable patterns of
sentiment expression.

Using the best-performing SVM model for each dataset, we analyzed the model’s
top-10 misclassified dialogues. Our findings, as presented in Table 5, reveal
that certain emotions are consistently misclassified across both the human-
generated and ChatGPT-generated datasets. Notably, emotions such as anxious,
surprised, trusting, jealous, apprehensive, sentimental, caring, hopeful,
faithful, and sad featured among the top-10 misclassified sentiments in each
dataset. This pattern underscores specific challenges in the binary valence
classification of certain emotion categories.

Table 4: Average Weighted F1-Scores for Valence Classification across 5-Folds. | Human-Generated Dialogues | ChatGPT-Generated Dialogues
---|---|---
Classifier | Avg. Weighted F1 | Std. Dev. F1 | Avg. Weighted F1 | Std. Dev. F1
Random Forest | 0.8778 | 0.0034 | 0.9324 | 0.0032
SVM | 0.9000 | 0.0023 | 0.9527 | 0.0036
MLP | 0.8953 | 0.0045 | 0.9579 | 0.0037

The consistent misclassification of certain emotions in our experiments
suggests inherent complexities in valence classification. We hypothesize that
this challenge is partly due to the ambiguity and overlap in emotional
expressions. For instance, emotions like sentimental, surprise, or hopeful
often exhibit characteristics of both positive and negative sentiments, making
them difficult to categorize in a binary system. Additionally, the context-
dependent nature of emotions can lead to potential valence misclassifications.
Moreover, the complexity of human emotions such as jealousy or caring,
underscores the limitations of a binary valence framework in capturing the
full range of human emotions. These factors highlight the need for more
nuanced classification approaches. Our classification only uses emotional
valence. Future research may use additional dimensions, such as arousal, to
analyze embeddings.

In addition to classification experiments, we utilized UMAP to visualize the
embeddings from both human-generated and ChatGPT-generated dialogues datasets.
UMAP, a dimensionality reduction technique effective at preserving global and
local spatial information [15], was used to project these high-dimensional
embeddings into a three-dimensional space. This approach facilitates visual
exploration of the distribution of embeddings with respect to the valence
categories. The resulting UMAP visualizations are shown in Figure 2(a) for the
human-generated dataset and Figure 2(b) for the ChatGPT-generated dataset. In
these figures, data points (representing each dialogue) are color-coded based
on the corresponding valence—positive or negative—as designated by the
aforementioned mapping function from the 32 original emotion categories. These
visualizations offer an empirical view of the valence clustering within the
embeddings, thereby providing a supplementary perspective to our analysis in
understanding the valence classification capabilities of the embeddings.

The UMAP projections of both the human-generated and ChatGPT-generated
dialogues reveal notable differences in cluster separation. Specifically, the
ChatGPT model demonstrates a more distinct separation of clusters, as
indicated by a higher Dunn Index value of 0.222, compared to 0.153 for the
human-generated dialogues. This metric, which quantifies cluster separation
based on the minimization of intra-cluster distances and maximization of
inter-cluster distances, suggests that the ChatGPT-generated dialogues exhibit
a clearer delineation between valence categories.

In the UMAP projection of the human-generated dialogues, we observed an
outlier blue cluster. This cluster represents a specific subset of emotions or
dialogue characteristics that are distinctly separate from the main clusters.
Within this outlier cluster, emotions such as prepared, caring, trusting,
proud, and impressed coexist alongside negative valence emotions like afraid,
terrified, lonely, and devastated. This phenomenon may occur in human
dialogues and not in ChatGPT dialogues because human emotional expression
often encompasses a complex and nuanced blend of sentiments, reflecting the
intricate nature of human psychology and social interactions. In contrast,
ChatGPT, while advanced, might not capture the same depth and subtlety in
emotional expression due to its algorithmic foundations and training data
constraints. Consequently, human dialogues exhibit a richer, more varied
emotional landscape where seemingly contradictory emotions can coexist within
the same context, leading to such unique clustering. This complexity is less
pronounced in ChatGPT-generated dialogues, which tend to follow more
predictable and uniform patterns of emotional expression. The presence of this
unique grouping within the human-generated dialogues, and its relative absence
in the ChatGPT-generated dialogues, further underscores the variation in how
valence categories are represented and separated in embeddings from the two
datasets. The interactive UMAP visualization code will be made available via
the GitHub link provided in Section 4 for further detailed analysis.

Table 5: Comparison of the top-10 misclassification frequencies in the valence
analysis: human-generated dialogues (2,180 out of 19,533) vs. ChatGPT-
generated dialogues (1,132 out of 19,533). There are certain emotions, such as
‘anxious’, that more frequently result in valence (positive or negative)
misclassifications in both datasets.

Human-Generated Dialogues | ChatGPT-Generated Dialogues
---|---
Emotion | Misclassified | Emotion | Misclassified | Emotion | Misclassified | Emotion | Misclassified
anxious | 224 | sentimental | 119 | trusting | 137 | anticipating | 72
surprised | 211 | caring | 119 | caring | 133 | apprehensive | 65
trusting | 170 | hopeful | 101 | surprised | 132 | anxious | 61
jealous | 168 | faithful | 90 | sentimental | 78 | hopeful | 52
apprehensive | 127 | sad | 71 | jealous | 74 | faithful | 52

(a) Human Dialogues

(b) ChatGPT Dialogues

Figure 2: 3-D UMAP visualizations of human- and ChatGPT-generated dialogues.
The dialogues are color-coded by positive or negative valence values,
determined by each dialogue’s underlying emotion category.

##  4 Summary and Discussion

This study explores linguistic differences between human and LLM-generated
dialogues, specifically focusing on ChatGPT-3.5 in comparison to the
EmpathicDialogues dataset. Using LIWC analysis across 118 linguistic
categories, we analyzed 19.5K dialogues. Our findings show that while human
dialogues exhibit greater variability and authenticity, ChatGPT demonstrates
superior proficiency in areas like social processes, analytical style,
cognition, attentional focus, and positive emotional tone, echoing the
narrative that LLMs can be “more human than human” in many aspects of language
use [9]. A key contribution of this research is the development of the
2GPTEmpathicDialogues dataset, a novel collection of ChatGPT-generated
dialogues, which serves as a valuable resource for exploring AI language
modeling. Additionally, the study reveals implicit coding of affect in
dialogue embeddings, despite no direct mentions of affect, highlighting the
emotional intelligence of AI. This research not only contributes to our
understanding of the linguistic capabilities of ChatGPT but also plays a
crucial role in informing the ongoing efforts to distinguish between human and
AI-generated text.

As AI masters human language and enters the realm of social interactions, we
face a future where distinguishing between conversations with humans and AI
may become increasingly challenging. Our findings demonstrate that ChatGPT’s
linguistic proficiency, in many aspects, surpasses human capabilities.
Language proficiency is one of the markers of human excellence, and language
is a distinguishing feature of our humanity and identity as social beings. Our
analysis shows that on cognitive features like analytical thinking, cognition,
and attentional focus, ChatGPT scores higher than humans. Given that computers
are not vulnerable to human variability, such as individual differences and
fatigue, this consistency offers significant advantages in contexts demanding
constant cognitive engagement and analytical precision.

However, this consistency could also be perceived as a weakness in situations
where human-like variability and adaptability are valued. The essence of human
communication often lies in the subtleties - the unstructured, the
unpredictable, and the emotional nuances - that are not purely cognitive
prowess but involve a complex interplay of factors, including empathy,
cultural context, and personal experiences. While ChatGPT demonstrates
impressive capabilities in mimicking these aspects to a certain extent, the
question remains whether it can fully replicate the depth and richness of
human interactions.

In the social realm, where generative AI is increasingly deployed, from
customer service to medical service, the implications of our findings are
significant. ChatGPT’s advanced linguistic abilities could enhance user
experiences by providing more coherent, contextually relevant, and emotionally
attuned interactions. Nevertheless, it also raises ethical considerations
about AI’s role in human interactions, particularly concerning authenticity
and the potential replacement of human roles in certain areas.

Limitations The findings from this study must be evaluated along with some
limitations. LLM models are improving rapidly, and our claims are limited only
to ChatGPT-3.5-Turbo. Newer versions such as GPT-4, Claude, and Gemini may
have performed differently. In the future, it would be beneficial to compare
the linguistic features of different LLMs and add to the companion dataset
generated for this study.

Using the default word limits in ChatGPT resulted in longer conversations that
averaged 300 words, which is significantly more than the average of 60 words
for human dialogues. This difference can be corrected in future research by
adjusting the word limit parameter. Despite this limitation, the findings from
our study are still valid because LIWC metrics adjust for the number of words
in the corpus by converting the metrics into a percentage of the number of
words.

In our valence analysis of the embeddings, we used an emotion classifier that
relied on the dichotomous classification of the 32 emotions into positive or
negative valence. This may be an overly limiting classification, which was
evident in the UMAP visualizations and classification experiment metrics.
Because emotions are complex, and we know from the co-activation theory of
emotions that it is possible to have both positive and negative emotions
concurrently, additional work is needed to allow for a subtler classification
with more emotional dimensions. For example, the arousal dimension can be
introduced in future research.

Another limitation arises from the inherent nature of the LIWC tool itself.
While LIWC is a widely used tool for linguistic analysis, it may not capture
the full spectrum of linguistic subtleties and nuances. This could limit the
depth of our understanding of the differences between human and AI-generated
languages. Despite our best efforts to mitigate role confusion through prompt
engineering, we were not able to completely eliminate it. This is a limitation
that must be addressed in future research.

Overall, our findings contribute to the emerging literature on LLMs through a
rigorous comparison of linguistic features of human conversations and ChatGPT
conversations. As AI evolves, the line between human-generated and AI-
generated content will continue to blur. The findings from our study show that
this has already occurred to a large extent. How we respond to this dissolving
identity is a larger philosophical question that extends beyond language and
will likely shape our future.

Data & Software Availability: Software and data resources utilized in the
studies presented in this paper can be accessed via the following link:
https://github.com/morganlee123/2GPTEmpathicDialogues. LIWC is a proprietary
software and must be obtained separately.

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