# PersonalityChat: Conversation Distillation
for Personalized Dialog Modeling with Facts and Traits

Ehsan Lotfi, Maxime De Bruyn, Jeska Buhmann, Walter Daelemans
CLiPS Research Center
University of Antwerp, Belgium
<EMAIL_ADDRESS>
###### Abstract

The new wave of Large Language Models (LLM) has offered an efficient tool to
curate sizeable conversational datasets. So far studies have mainly focused on
task-oriented or generic open-domain dialogs, and have not fully explored the
ability of LLMs in following complicated prompts. In this work, we focus on
personalization, and employ LLMs to curate a dataset which is difficult and
costly to crowd-source: PersonalityChat is a synthetic conversational dataset
based upon the popular PersonaChat dataset, but conditioned on both personas
and (Big-5) personality traits. Evaluating models fine-tuned on this dataset,
we show that the personality trait labels can be used for trait-based
personalization of generative dialogue models. We also perform a head-to-head
comparison between PersonalityChat and PersonaChat, and show that training on
the distilled dataset results in more fluent and coherent dialog agents in the
small-model regime.

##  1 Introduction

Training deep learning models and architectures to act as decent dialog agents
is a data-hungry task. Historically, big conversational datasets have been
either curated from already existing dialog or sudo-dialog resources (e.g.
Reddit Dziri et al. (2019), Ubuntu Lowe et al. (2015), OpenSubtitles Lison and
Tiedemann (2016)), or via crowd-sourcing, which is usually the case if
specific features are desired (e.g. persona-grounded Zhang et al. (2018),
empathetic Rashkin et al. (2019)). But these methods are noisy, costly or
limited in the scope of feasible grounding features.

Persona:
---
I like to tan in tanning beds.
I love spending money.
I am high maintenance.
I diet a lot.
I love listening to Britney Spears.
Personality Traits (ChatGPT):
Openness: No clues.
Conscientiousness: Probably low, given the
statement I love spending money and that
they admit to being high maintenance.
Extraversion: Probably high, as they enjoy
tanning and listening to music, both of which
are often done in social settings.
Agreeableness: No clues.
Neuroticism: Probably high, as they state
that they diet a lot and may have insecurities
about their appearance.
Table 1: Example of (Big-5) personality traits and explanations generated by
ChatGPT when prompted with persona. We then sample the speculated trait
labels, and use them along with personas to prompt ChatGPT for personalized
dialogs.

Recently, Large Language Models (LLMs) like GPT-3 Brown et al. (2020),
ChatGPT, GPT-4 and OPT Zhang et al. (2022) provided a cheaper and more
flexible alternative, which has been rapidly adopted to augment existing
datasets Chen et al. (2022); Mehri et al. (2022); Sahu et al. (2022) or
generating new ones Rosenbaum et al. (2022); Kim et al. (2022); Chen et al.
(2023). These studies have been mainly focused on task-oriented dialog, or
scaling; i.e. exploiting the compelling efficiency and skill of the latest
models to generate large-scale quality datasets that can help to mitigate some
of the shortcoming of current dialog agents (e.g. common sense knowledge Kim
et al. (2022)). However the potential of these models in assuming
sophisticated conversational settings has not been fully explored.

In this study, we exploit this flexibility to distill a corpus which is
difficult and expensive to curate from human interactions: a conversational
dataset conditioned on both personas and the Big-5 personality traits. To
prompt the LLM, we employ persona statements from the popular PersonaChat
dataset Zhang et al. (2018) and enrich them with relevant labels from the
well-known Big-5 personality traits McCrae and John (1992) which we also
obtain using LLMs. Besides generating the first sizeable dialog corpus curated
explicitly with personality traits, this parallel approach allows us to
perform a fair comparison between the crowd-sourced PersonaChat and its
distilled counterpart.

Our contributions are as follows: 1) We release PersonalityChat; a distilled
personalized dialog dataset grounded on persona and personality traits. 2) We
show that personality trait labels can be used to affect and modify the
‘attitude’ of a dialog agent. 3) We demonstrate that in the small-model
regime, training on PersonalityChat results in better performing dialog
agents. 4) We also release PersonaTraits; a dataset of personality trait
speculations, generated by ChatGPT for various personas111Datasets will be
available at https://github.com/ELotfi/PersonalityChat..

##  2 Related Work

###  2.1 Dialog Distillation from LLMs

Early attempts to generate synthetic dialogues often employed schemes, agendas
and templates to simulate data, mostly limited to task-oriented dialog
Schatzmann et al. (2007); Shah et al. (2018); Campagna et al. (2020).
Recently, the in-context zero-shot and few-shot learning capabilities of large
language models, has made it possible to generate fluent customizable
conversations either from scratch, or by providing a few examples. Kim et al.
(2022) prompted InstructGPT Ouyang et al. (2022) with simple narratives based
on common sense statements to curate a large social dataset (SODA). Chen et
al. (2023) used in-context learning, prompting the OPT Zhang et al. (2022)
model with a combination of topic, facts and dialog examples to generate
another social dialog dataset extended to multi-party conversation (PLACES).
Lee et al. (2022) proposed a pipeline to augment and update PersonaChat with
new personas using GPT-3. More recently, MathDial Macina et al. (2023) used a
semi-synthetic framework to generate dialogs grounded in math reasoning
problems by pairing real teachers with LLM-simulated students.

###  2.2 Personalized Dialog Generation

Before neural methods became mainstream, studies like Mairesse and Walker
(2007, 2008a, 2008b) and Gill et al. (2012) used parameterised symbolic
methods to incorporate the big-5 personality traits in dialog generation based
on lexical features. In one of the first attempts for neural personalised
dialog generation, Li et al. (2016) used jointly learned Speaker embeddings to
inject personal information into the response. The release of PersonaChat
Zhang et al. (2018) as the first large scale dataset grounded in personal
facts, canonised the factual approach to dialog personalization. Curating a
sizeable human-written dialog dataset grounded in personality traits is
difficult and expensive. Recently Saha et al. (2022) exploited two trait-
annotated non-conversational corpora (PANDORA Gjurković et al. (2021) and
Essays Pennebaker and King (1999)) to train a classifier which was used to
annotate two existing knowledge-based datasets with trait labels. These
annotations then were incorporated to train a personalised model.

###  2.3 The Big-5 Personality Traits

Rooted in the Lexical Hypothesis Galton (1884) and developed over the last
century, the Big Five personality traits Norman (1963); Goldberg (1990),
consist of Openness (to experience), Conscientiousness, Extraversion,
Agreeableness and Neuroticism. These traits were repeatedly obtained
empirically by extracting the main components of a factor analysis over
adjective descriptors, and therefore considered as the dimensions explaining
the most variance of behaviour among people Mairesse and Walker (2008b).
Although the Big Five model has limitations Paunonen and Jackson (2000), it
has strong links to biological bases Revelle (2003) and is shown to be a
stable personality evaluation metric Cobb-Clark and Schurer (2012). Table 11
in the Appendix (from McCrae and John (1992)) lists some of the defining
adjective and attributes for the five dimensions.

Figure 1: Overall pipeline to curate PersonalityChat. First (left) ChatGPT is
used to predict personality traits for PersonaChat personas. Then (right)
sampled traits (based on the predictions) are used along with the personas to
prompt ChatGPT for dialogs that are grounded on both persona facts and
personality traits.

##  3 PersonalityChat

Generating a diverse set of conversations from LLMs is usually done using a
varied collection of scenarios or settings which provide the model with
background information. For example Kim et al. (2022) employed short
narratives based on commonsense knowledge, and Chen et al. (2023) used a list
of topics and tasks from the FITS dataset Xu et al. (2022). We base our work
on PersonaChat Zhang et al. (2018), the most popular dataset for dialog
personalization which was curated by pairing online crowd-source workers and
asking them to converse with each other using their assigned personas; a small
set of simple personal statements about occupation, interests, hobbies etc.
(e.g. Table 1-top). Using PersonaChat allows us to 1) exploit persona
statements to acquire (estimated) personality traits, as well as dialog
scenarios, 2) perform a head-to-head comparison between crowd-sourcing and
distillation, as means of dialog dataset curation.

The overall 2-step curation pipeline is shown in Figure 1: first ChatGPT is
employed to predict personality traits for personas from the PersonaChat
dataset. Then traits are sampled from these labels and used along with the
personas to prompt ChatGPT to generate dialogs grounded on both features. Next
we describe these two steps in detail.

###  3.1 Personality Trait Prediction

The PersonaChat dataset Zhang et al. (2018) comprises of 10,907 dialogs
(8939/1000/968 for train/validation/test) between crowd-source workers. Dialog
samples come with a pair of Personas labeled as ‘self’ and ‘other’, each
containing 4-6 personal facts similar to what is shown in Table 1-Top. Not all
personas are unique and for example the ‘self’ personas are permuted from
4333/409/414 unique sets.

Personas provide enough background information to prompt LLMs for social
dialog and in theory we can just enrich this information with randomly sampled
personality traits to generate our prompts. But since persona facts and
personality traits are not independent, in order to better align our (trait)
sampling with personas, we first use ChatGPT to acquire trait label
estimations for each persona, as one of ‘probably low’, ‘probably high’ or ‘no
clues’. (The exact prompt, and examples of the predicted labels and supporting
explanations can be found in Appendix A) and Table D respectively). Figure 2
shows the trait-label distribution, indicating that in 4 out of 5 traits, ‘no
clues’ is the majority label. On average personas have 2.3 indicative labels
(i.e. ’probably low’ or ’probably high’)222We release the acquired dataset as
PersonaTraits, including 5156 samples of persona, 3-way labels and
explanations..

Figure 2: Distribution of the estimated trait labels for PersonaChat personas Method |  Ave. # indicative labels |  Ave. Hamming distance |  Ave. polar Hamming distance
---|---|---|---
Human |  $2.35\pm.14$ |  - |  -
ChatGPT |  $2.27$ |  $1.88\pm.06$ |  $.13\pm.01$
random |  $3.45$ |  $3.42\pm.1$ |  $.85\pm.04$
Table 2: Comparing human annotations for persona-based traits with ChatGPT and
random labels on 100 samples. Distances are measured from the human
annotations. Polar Hamming distance only considers polar or extreme
disagreement; i.e. high vs. low.

Predicting trait labels from a few facts is inherently noisy; in most cases
persona statements do not strongly imply or dictate a specific trait, which
means that the actual label is a broad distribution. Since we only use these
labels for sampling to prompt the LLM (as an alternative to random labels),
what is relevant to us is to make sure that: 1) the labels are in fact closer
to human annotations than random sampling, 2) they have relatively low polar
disagreements (‘high’ vs. ‘low’ for a trait) with the human annotation. To do
so, we ask two expert annotators to label a set of 100 personas with the same
categories (‘probably low’, ‘probably high’ or ‘no clues’). Table 2 shows how
these annotations compare with ChatGPT and random labels. The difference is
measured using the Hamming distance (for five traits), and the polar distance
only considers extreme disagreement. As can be seen, ChatGPT labels are
clearly closer to human annotations, and while the exact agreement is not very
high, the polar Hamming distance is quite low (.13 on average). In the next
step, we use personas and their acquired trait labels to prompt ChatGPT for
social conversations.

###  3.2 Personalized Dialog Generation

####  3.2.1 Sampling Trait Labels

Although we can use labels as they are, we choose to sample based on them to
control the complexity, as some personas have 4 or 5 indicative personality
labels, which can impose too many constraints for a relatively short dialog.
In addition, since we are using the same model (ChatGPT) for both steps, we
speculate that introducing some randomness would smooth potential lexical
links and biases between personas and their estimated traits, and encourage
the model to explore more sophisticated ways to reflect the traits in
utterances, rather than relying solely on the relevant personal facts.
Therefore, for each persona with $k$ indicative trait-labels (i.e. ‘high’ or
‘low’), we sample $n$ ($0<n<4$) indicative labels, following these rules:

  * •

If $n<=k$, we randomly pick $n$ labels.

  * •

Otherwise, we compensate the difference by randomly switching $n-k$ non-
indicative labels (i.e. ‘no clues’) to an indicative one.

####  3.2.2 Prompting

Nest step is prompting ChatGPT with personas and personality traits for
grounded dialogs. This can be done with ‘single agent’ –where one prompt is
made with information for both speakers and the model is asked to generate a
conversation between them in one pass–, or ‘double agent’ where each speaker
gets their own API (prompted with corresponding information) and the
conversation is generated one utterance at a time going back and forth between
the agents with updated contexts. In theory the double agent approach sounds
more faithful to the real dynamics of a conversation333As well as the crowd-
sourcing method in PersonaChat but according to our trials, it often triggers
the ‘assistant’ side of ChatGPT (resulting in long and ‘helpful’ utterances!)
which is hard to mitigate with prompts. Moreover, it is not easy to anticipate
or control the length of conversation as it might ‘end’ before the generation
loop is over. The single agent approach on the other hand is simpler and
cheaper (on average consuming almost 1/10 tokens) but it is prone to persona
‘leakage’; i.e. one speaker referring to the other speaker’s information.
Another challenge (in both generation settings) is avoiding explicit
grounding; i.e. cases where the speaker talks about their personality traits
in a self-aware and superficial manner (e.g. “I am a very conscientious
person.”), instead of implicitly reflecting them in responses. We realize that
both these issues, can be considerably mitigated by prompt engineering, but
not totally eradicated.

After many trials, we opt for the single agent method, plus a lexical post
processing step to remove (and regenerate) the most ‘leaky’ and ‘explicit’
cases. To control the complexity, we only provide personality traits for the
second speaker, corresponding to the ‘self’ persona (or Person2 in our
terminology). We generate a dialog for each PersonaChat sample (i.e. persona
pair), picking the temperature uniformly from the $(.25,1.25)$ interval and
limiting the number of generated tokens to 450. The exact prompt can be found
in Appendix A.

###  3.3 Dataset Statistics

####  3.3.1 Lexical Features

Table 3 compares various lexical properties of the resulted dataset
(PersonalityChat) with those of PersonaChat. MTLD is the Measure of Textual
Lexical Diversity McCarthy and Jarvis (2010), calculated as the mean length of
sequential word strings in a text that maintain a given TTR value444We use the
same TTR value (.72) as employed by the authors McCarthy and Jarvis (2010).
For the rationale see the paper., and can be interpreted as an indication for
local diversity. Based on the numbers, PersonalityChat has a smaller
vocabulary555The fact that PersonaChat is not normalized and contains
erroneous and non-standard spellings contributes to this difference. but a
higher MTLD score, which points to a discrepancy in how the inter- and intra-
dialog diversity compare between the two datasets. To see how verbatim the
persona facts have been employed in dialogs, we measure the average maximal
common n-gram length (MCN) between persona facts and utterances or whole
dialogs666For example MCN=3 for dialog means that on average, the longest
n-gram shared between individual persona facts and the corresponding dialog
has a length of 3 (tokens)., which results in a higher value for
PersonalityChat, specially over the whole conversation.

The dialog example in Table 15 demonstrates some aspects of the lexical and
stylistic differences: The language usage in the crowd-sourced example
(PersonaChat) is more creative, specially in the way that persona facts are
being incorporated. This however comes with a significantly higher variance,
and while synthesized dialogs almost always follow a safe and streamlined
style of fact-grounded personal conversation, the crowd-sourced samples vary
between exemplary ones like Table 15, and low quality cases where persona
facts are just exchanged with little or no conversational context.

####  3.3.2 Curation Cost

Table 4 shows the generation cost for both steps, i.e. personality trait
prediction, and dialog generation, which overall amounts to 15$ and 70hrs.
This excludes trials and corrective re-generations which in our case add an
extra 5 %.

| PersonaChat | PersonalityChat
---|---|---
Dialogs | 10,907 | 10,907
Turns (avg. dialog) | 14.8 | 17.3
Tokens (avg. turn) | 11.7 | 13.7
MTLD | 50.3 | 69.1
MCN (dialog) | 1.76 | 3.38
MCN (turn) | 1.23 | 2.0
Vocab size (train) | 18,669 | 11,104
Table 3: Lexical comparison between PersonaChat Zhang et al. (2018) and our distilled PersonalityChat dataset. MTLD stands for Measure of Textual Lexical Diversity McCarthy and Jarvis (2010) and MCN is the average maximal common n-gram between persona and dialog (or turn), as described in 3.3. | Step 1 | Step 2 | Overall
---|---|---|---
| ps | all | ps | all | ps | all
Time | 6s | 8.6h | 20s | 61h | 23s | 70h
Cost | .05¢ | 2.6$ | .1¢ | 12$ | .13¢ | 15$
Table 4: Curation cost for both steps in our method; i.e. personality trait
prediction (step 1) and dialog generation (step 2). ‘ps’ is the ‘per sample’
value, which does not add up to the last column since each persona is used in
almost two dialogs.

##  4 Training with PersonalityChat

In this section we perform experiments to answer the following questions:

  * •

RQ1: Can we use personality traits to control the conversational behavior of a
model?

  * •

RQ2: How do the models trained on PersonaChat and PersonalityChat compare in
performance? (only using persona)

###  4.1 RQ1: Controlling Personality Traits

To answer the first question, we fine-tune a BART model777The large version
accessible via HuggingFace’s transformers as facebook/bart-large Lewis et al.
(2020) on our dataset, using both personas and personality traits in the
context. Following the common practice888Using specific tokens and phrases to
control language generation is inspired by similar works, specially CTRL
Keskar et al. (2019)., we compose the inputs by concatenating different
segments as <traits> {trait labels} <persona> {persona facts} {history}, where
history includes previous turns marked by <user> and <agent>, and <traits>,
<persona>, <user> and <agent> are special tokens added to the vocabulary. We
do the fine-tuning using HuggingFace’s transformers library Wolf et al. (2020)
for 8 epochs, while the validation loss declines (More details in Appendix B).

To see if the trained models are capable of generating trait-adapted
responses, we perform two sets of evaluations:

Figure 3: Comparing single-trait utterances based on simple lexical/semantic features: a) number of tokens, b) sentiment score, c) number of question marks, d) number of exclamation marks. The horizontal black line indicates the value in utterances generated without specifying any traits. Values are average over utterances. | Openness | Conscientiousness | Extraversion | Agreeableness | Neuroticism
---|---|---|---|---|---
Model | high | tie | low | high | tie | low | high | tie | low | high | tie | low | high | tie | low
Ave. vote | 66% | 18% | 16% | 36% | 40% | 24% | 64% | 20% | 16% | 72% | 14% | 14% | 42% | 46% | 14%
Cohen’s $\kappa$ | 0.56 | 0.29 | 0.46 | 0.49 | 0.25
Table 5:  Pairwise per-dialog comparison by human annotators between dialogs
generated using opposing trait labels (e.g. high and low openness). The second
row shows Cohen’s kappa between annotators.

####  4.1.1 Turn-wise Automatic Evaluation

For this part, we use the whole validation set (~8k samples) and generate
responses using single trait labels, or no labels (i.e. $5*2+1=11$ responses
for each sample, the last of which we consider as ‘neutral’). Then we compare
a number of indicative lexical/semantic features between the inferred
utterances, including average sentiment score, average number of tokens,
question marks and exclamation marks per utterance. For sentiment we employ a
BERT-base model finetuned on the 5-way review classification and use the class
probabilities to calculate an aggregated score999More specifically we
calculate $\Sigma_{i=0}^{4}s[i]*p_{i}$ where $s=[-1,-.5,0,.5,1]$.. The result
(Figure 3) shows a consistent pattern in which utterances generated with
higher openness, extraversion, agreeableness and conscientiousness, or lower
neuroticism are more expressive (a and d), positive (b) and engaging (c), with
the difference being relatively higher for openness, extraversion and
agreeableness. It also shows that overall, the ‘low’ label is more effective
in steering the response away from the neutral behavior (i.e. the no-trait
baseline marked with a black line in each graph).

To have a more semantic view, we identify the frequently ‘preferred’ and
‘avoided’ n-grams for each trait-label by comparing n-gram frequencies in the
trait and neutral utterances. The result ( Table 16 in the Appendix) shows
that the statistically indicative n-grams for each trait-label
combination101010This is measured by $abs(x-y)/y$ conditioned on $abs(x-y)>20$
where $x$ and $y$ are the occurrence frequency in the trait and neutral
corpora, respectively. generally agree with common semantic expectations for
that trait-label (cf. Table 11), although the diversity and richness varies
quite noticeably between different traits and labels. In particular, the ‘low
neuroticism’ corpora does not offer any particularly important n-grams.

\Eval | PChat | PTChat | Both
---|---|---|---
Train \ | PPL | F1 | TTR | MCN | H | PPL | F1 | TTR | MCN | H | PPL | F1 | TTR | MCN | H
PChat | 9.88 | .18 | .71 | 4.75 | 1.3 | 21.5 | .21 | .76 | 3.58 | 1.2 | 15.7 | .19 | .73 | 4.20 | 1.3
PTChat | 56.3 | .17 | .92 | 4.20 | 2.0 | 3.52 | .30 | .91 | 3.44 | 2.2 | 10.8 | .24 | .92 | 3.81 | 2.1
Both | 9.69 | .19 | .80 | 4.15 | 1.8 | 3.40 | .31 | .92 | 3.14 | 2.3 | 5.21 | .25 | .86 | 3.65 | 2.1

Table 6: Performance metrics for various train (rows)-evaluation (columns)
settings, using T5-small. PPL and TTR are perplexity and type/token ratio. MCN
is the average length of the maximal n-gram shared between response and
persona facts, and H is overall score (from 1 to 3) assigned by human
annotators.

| Naturalness | Coherence | Consistency | Specificity | Overall
---|---|---|---|---|---
Domain | PT | tie | P | PT | tie | P | PT | tie | P | PT | tie | P | PT | tie | P
PChat | 70% | 26% | 4% | 49% | 40% | 11% | 25% | 64% | 11% | 12% | 67% | 21% | 74% | 15% | 11%
PTChat | 66% | 34% | 0% | 64% | 36% | 0% | 29% | 62% | 10% | 13% | 70% | 17% | 81% | 17% | 2%
Ave. | 68% | 30% | 2% | 56% | 38% | 6% | 26% | 64% | 10% | 12% | 69% | 19% | 77% | 16% | 7%

Table 7: Head-to-head quality comparison by human annotators between the
output of T5-small models fine-tuned on PersonaChat (P), and PersonalityChat
(PT). Rows indicate the test data domain.

\Eval | PChat | PTChat | Both
---|---|---|---
Train \ | PPL | F1 | TTR | MCN | H | PPL | F1 | TTR | MCN | H | PPL | F1 | TTR | MCN | H
PChat | 7.56 | .21 | .85 | 3.82 | 2.3 | 13.9 | .25 | .88 | 2.50 | 2.1 | 10.9 | .23 | .87 | 3.16 | 2.2
PTChat | 52.0 | .18 | .94 | 3.36 | 2.7 | 2.60 | .36 | .94 | 2.95 | 2.8 | 8.81 | .27 | .94 | 3.15 | 2.8
Both | 7.52 | .21 | .88 | 3.40 | 2.4 | 2.55 | .38 | .94 | 2.79 | 2.7 | 3.96 | .30 | .91 | 3.10 | 2.6

Table 8: Performance metrics for various train (rows)-evaluation (columns)
settings, using T5-base.

| Naturalness | Coherence | Consistency | Specificity | Overall
---|---|---|---|---|---
Domain | PT | tie | P | PT | tie | P | PT | tie | P | PT | tie | P | PT | tie | P
PChat | 23% | 73% | 4% | 40% | 49% | 11% | 19% | 79% | 2% | 11% | 75% | 13% | 60% | 15% | 25%
PTChat | 20% | 80% | 0% | 64% | 30% | 6% | 21% | 72% | 7% | 19% | 79% | 2% | 79% | 10% | 11%
Ave. | 21% | 77% | 2% | 52% | 40% | 9% | 20% | 76% | 4% | 15% | 77% | 8% | 69% | 13% | 18%

Table 9: Head-to-head quality comparison by human annotators between the
output of T5-base models fine-tuned on PersonaChat (P) and PersonalityChat
(PT). Rows indicate the test data domain.

####  4.1.2 Pairwise Per-dialog Human Evaluation

To see if the observed statistical patterns lead to discernible semantic
differences during interaction, we conduct a human evaluation. Following Smith
et al. (2022) who argue for the advantage of per-dialog comparison (vs. per-
turn) in situations where differences between models only emerge after several
conversation turns, we simulate 100 full conversations with random lengths
between two models, one as user (trained on the ‘odd’ utterances without
traits), and one as agent (trained on the ‘even’ utterances with traits),
using persona pairs from the validation set. To mitigate the persona-trait
bias, we filter the persona pool, keeping only personas with maximum 2
indicative trait labels (i.e. labels other than ‘no clues’. See 3.1). For each
sampled persona pair, we generate a pair of conversations for every trait, one
with ‘high’ and one with ‘low’ label. This results in 500 dialog pairs,
generated with single opposing trait labels.

To evaluate the outcome, we ask two annotators —one psychology student
familiar with the topic, and one in-house annotator provided with generic
explanatory material on the Big-5 traits— to indicate in which dialog of a
pair, the agent shows a higher value for that trait, with a possibility for a
tie. Table 5 shows the average vote, along with annotator’s agreement (Cohen’s
kappa) for each trait, based on which we can conclude that the model is
capable of noticeably reflecting Openness, Extraversion and Agreeableness in
its output, while Conscientiousness and Neuroticism are not incorporated
enough. This is consistent with the automatic results (Figure 3). Table 17 (in
the Appendix) shows 5 pairs from our evaluation set, cherry-picked to
demonstrate how the model’s outcome is affected by trait labels.

###  4.2 RQ2: PersonaChat vs. PersonalityChat

To investigate the second question, we fine-tune sequence-to-sequence
pretrained transformers on these data (without the trait labels), and compare
the resulting models using automatic metrics as well as human evaluation.
Since we are interested in the low-parameter regime, we first select
T5-small111111More precisely, we use T5-v1.1-small which is an improved
version. which is one of the smallest pretrained encoder-decoders with around
77M parameters. For a fair comparison, we train on the odd utterances (first
speaker), since their responses are not grounded in personality traits in our
dataset. For brevity, we will refer to PersonaChat and PersonalityChat as
PChat and PTChat respectively, in the rest of this section.

To evaluate and compare the models, we infer responses from them for the PChat
and/or PTChat validation set, using a beam-search of size 5. For automatic
metrics, we employ PPL (validation perplexity), F1 (F1 measure between
generated and gold response), TTR (type/token ratio) and MCN (described in
3.3.1). We also ask in-house human annotators to score a 200-sample subset of
generated responses (100 for each domain) from 1 to 3, in terms of overall
quality1212123 being a response that is fluent, coherent, consistent and
specific. More details in Appendix C. (H column).

Table 9, summarizes the results for 9 different settings, depending on the
train (rows) and evaluation (columns) domains, which can be PChat, PTChat or
both. As can be seen, even with T5-small the in-domain perplexity of the
PTChat model drops to the very low value of 3.52 (as opposed to 9.88 for PChat
model). In terms of generation quality, training on PTChat (second row)
generally results in better metrics, even on the out-of-domain PChat data. In
particular, the PTChat model (second row) generates significantly less
repetitive responses than the PChat model (higher TTR), and acquires a lower
MCN score, which is interesting considering the higher MCN value in the PTChat
dataset (Table 3). Table 9 also shows that training on both datasets (last
row) improves the PChat performance more than PTChat (PChat column: row 3 vs.
row 1, compared to PTChat column: row 3 vs. row 2).

To have a more detailed result, we perform head-to-head comparison between the
outputs of these models, from the same 200 samples: We ask our annotators to
pick the ‘better’ response in terms of Naturalness, Coherence, Consistency,
Specificity and Overall quality, with a possibility for a tie131313More
details in Appendix C.. Table 9 shows that the PT model (trained on
PersonalityChat) beats the P model (trained on PersonaChat) on both domains in
the overall preference, as well as specific features, except for Specificity.
In particular, PT generates significantly more natural and coherent responses
than P. Table 12 shows this advantage still holds in some categories over the
PPT model (trained on both datasets), although with a narrower margin.

To see whether these results generalize to bigger models, we repeat the
experiments using T5-base (~220M parameters). The results (Tables 9, 9 and 13)
show that while PChat benefits more from the increase in model size, PTChat
still holds its overall advantage.

##  5 Conclusion

In this work we exploited the highly customizable generation skills of ChatGPT
to build a corpus which is difficult and expensive to curate from human
interaction; i.e. a sizeable dialog dataset grounded in both personal facts
and (Big-five) personality traits. Starting from the PersonaChat dataset, we
enriched the persona statements with personality trait speculations and used
them to prompt ChatGPT for personal dialog. We explored the qualities of this
dataset in two parts: 1) trait modification, which showed that the overall
‘attitude’ of a dialog agent trained on our dataset can be modified using
trait labels, specially for Openness, Extraversion and Agreeableness. 2)
comparison with PersonaChat (using only persona facts) made possible by the
parallel one-to-one curation scheme, which showed our distilled dataset can be
used to train smaller dialog models with better performance, specially in
terms of naturalness and coherence.

We also faced and discussed some of the potential shortcomings of our
approach. Most importantly, the ‘easy-training’ advantage of a distilled
dataset comes with a less diverse and more predictable language distribution
which can make a dialog agent sound ‘boring’ on the long run. Combining the
natural and synthetic datasets can be a solution but (at least in our
experiments) the distilled distribution seems to mostly overrule the natural
one. In trait modification, we observed interesting results in the way trait
labels can affect and steer model’s conversational behavior but there is a lot
of room for improvement (e.g. Table 5). One idea for example might be making
use of the explanatory PersonaTraits dataset, which we only released as a side
product in this study.

## Limitations

The first limitation of our work regards various biases that are incorporated
in the curated data as the result of our choices. Most importantly ChatGPT
introduces its (regularised) preferences, specially in dialog generation.
Employing a non-proprietary model would have given us more control over the
generation process but this comes with significantly less flexibility, and a
necessary post-processing step to check for outliers and harmful content. On a
more subtle level, using PersonaChat personas to seed personality traits is
not ideal since their generally expressive content and cold/neutral tone,
bears implications regarding personality traits which affects the trait
distribution.

Our evaluation process, although standard and relatively comprehensive, has
limitations which are mostly common in dialog assessment. The automatic
metrics are calculated over responses generated from validation or test
context which does not necessarily generalise to the real world interaction
with an agent. More particular to our work, is the way we evaluated trait
modification by using only one trait label at a time, which although makes
calculations easier and more reliable, does not necessarily reflect the
model’s behavior when prompted with multiple trait labels. The potential
persona-personality correlation is another influencing parameter in this step,
which although we tried to mitigate by using more neutral persona sets, is not
completely resolved.

Finally and as mentioned in the previous section, the trait incorporation in
generated dialogs is still basic and rather superficial, which only leaves
more room for future studies and improvements.

## Acknowledgements

This research received funding from the Flemish Government under the
“Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” programme.

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##  Appendix A Appendix: Prompts

For the first step (trait speculation), we prompt ChatGPT with: These are some
personal statements about/by <A>:{persona} Can you give a rough speculation on
how <A> might score in the Big Five personality traits? Try to summarize the
response for each category as one of ’probably low’, ’probably high’ or ’no
clues’. Mentioning ‘rough speculation’ proves to be crucial to get labels
since otherwise the model tends to refuse an answer, pointing out the
impossibility to label people based on limited data.

For the second step (dialog generation), we build the prompts from the
following template: Generate a conversation between Person1 and Person2 with
the following personas:\n Person1: {persona1}.\n Person2: {persona2}. Also
bear in mind that Person2 has {sampled_trait_labels}. Person2 is NOT self-
aware about these traits (so DO NOT directly talk about them) but they can be
grasped indirectly from their responses. \n They are chatting online but they
don’t know each other beforehand and they do NOT see each other’s personas.\n
Continue the conversation for 12-16 utterances. Keep the utterances natural
and short (maximum 2 sentences). The ‘12-16’ constraint is the same as for
PersonaChat and sampled_trait_labels refers to the verbalized labels, e.g.
‘high openness and low extraversion’).

##  Appendix B Appendix: Training Details

We train the models using the trainer method in HuggingFace’s transformers
library, for 8 epochs with an effective batch size of 128 and a linearly
decaying learning rate with a maximum value of 1e-4 for T5 and 2e-5 for BART
models. In building the samples, we keep the last 4 turns in the context.

##  Appendix C Appendix: Human Evaluation (RQ2)

For the human evaluation of generated responses, we presented two in-house
annotators with response pairs inferred from compared models using the same
input, and asked them to:

  * •

Indicate which response (if any) is more:

Natural; i.e. meaningful, fluent and non-repetitive utterance with correct
grammar

Coherent; i.e. the utterance is a valid continuation of the previous context
(disregarding consistency)

Consistent; i.e. the utterance is not contradicting any previously stated
facts including agent’s persona

Specificity; i.e. the utterance is not a generic and bland phrase and makes
use (if possible) of the relevant facts.

Overall; the utterance is preferred in general.

  * •

Score the overall quality of each response with 1,2 or 3, where:

3 means a response that is natural, coherent, consistent and specific.

2 means a response that lacks in one of the four features above.

1 means a response that lacks in two or more of the four features.

Table 10 shows the Cohen’s kappa value as a measure of annotators’ agreement
for different tests reported in Section 4.2.

Experiment | Nat. | Coh. | Con. | Spc. | Ovr. | PT score | P/PPT score
---|---|---|---|---|---|---|---
T5-small, PT vs. P (Table 9) | .73 | .66 | .82 | .68 | .77 | .67 | .71
T5-small, PT vs. PPT (Table 12) | .84 | .77 | .83 | .70 | .69 | .65 | .56
T5-base, PT vs. P (Table 9) | .65 | .84 | .78 | .7 | .74 | .71 | .71
T5-base, PT vs. PPT (Table 13) | .78 | .71 | .82 | .76 | .75 | .67 | .70
Table 10: Cohen’s kappa for different evaluation experiments. P, PT and PPT
respectively refer to models trained on PersonaChat, PersonalityChat and both.

##  Appendix D Appendix: Additional Tables

Personality Trait | Adjectives | Scales
---|---|---
Openness |  0.35Artistic, Curious, Imaginative, Insightful, Original, Wide interests |  0.4Fantasy, Aesthetics, Feelings, Actions, Ideas, Values
Conscientiousness |  0.35Efficient, Organized, Planful, Reliable, Responsible, Thorough |  0.4Competence, Order, Dutifulness, Achievement Striving, Self-Discipline, Deliberation
Extraversion |  0.35Active, Assertive, Energetic, Enthusiastic, Outgoing, Talkative |  0.4Warmth, Gregariousness, Assertiveness, Activity, Excitement seeking, Positive emotions
Agreeableness |  0.35Appreciative, Forgiving, Generous, Kind, Sympathetic, Trusting |  0.4Trust, Straightforwardness, Altruism, Compliance, Modesty
Neuroticism |  0.35Anxious, Self-pitying, Tense, Touchy, Unstable, Worrying |  0.4Anxiety, Hostility, Depression, Self-consciousness, Impulsiveness, Vulnerability
Table 11: Examples of defining adjectives and scales for the big-five traits (from McCrae and John (1992) | Naturalness | Coherence | Consistency | Specificity | Overall
---|---|---|---|---|---
Domain | PT | tie | PPT | PT | tie | PPT | PT | tie | PPT | PT | tie | PPT | PT | tie | PPT
PChat | 41% | 53% | 6% | 36% | 40% | 24% | 11% | 74% | 15% | 13% | 68% | 19% | 49% | 17% | 34%
PTChat | 9% | 87% | 4% | 9% | 82% | 9% | 4% | 83% | 13% | 13% | 85% | 2% | 13% | 64% | 23%
Ave. | 25% | 70% | 5% | 23% | 61% | 17% | 7% | 78% | 14% | 13% | 76% | 11% | 31% | 40% | 29%
Table 12: Head-to-head quality comparison by human annotators between the output of T5-small models fine-tuned on PersonalityChat (PT), and PersonaChat + PersonalityChat (PPT). Rows indicate the test data domain. | Naturalness | Coherence | Consistency | Specificity | Overall
---|---|---|---|---|---
Domain | PT | tie | PPT | PT | tie | PPT | PT | tie | PPT | PT | tie | PPT | PT | tie | PPT
PChat | 13% | 83% | 4% | 34% | 58% | 8% | 10% | 90% | 0% | 7% | 70% | 23% | 55% | 24% | 21%
PTChat | 4% | 96% | 0% | 15% | 81% | 4% | 0% | 98% | 2% | 6% | 92% | 2% | 21% | 66% | 13%
Ave. | 9% | 89% | 2% | 25% | 69% | 6% | 5% | 94% | 1% | 7% | 81% | 13% | 38% | 45% | 17%
Table 13: Head-to-head quality comparison by human annotators between the
output of T5-base models fine-tuned on PersonalityChat (PT), and PersonaChat +
PersonalityChat(PPT). Rows indicate the test data domain.

Persona |  Speculated Personality Traits (by ChatGPT)
---|---
0.25- my favorite music genre is death metal. |
\- i am a swim coach at my local pool. |
\- my mother is very traditional while i prefer to be more free spirited. |
\- my family and i are from india. |  0.72Openness: Probably high. <A> seems to have a preference for a music genre that is considered unconventional or niche, suggesting an openness to new experiences and unconventional ideas.
Conscientiousness: No clues. |
Extraversion: No clues. |
Agreeableness: Probably low. The contrast between <A>’s free-spiritedness and their traditional mother suggests a potential conflict in values or beliefs, which could indicate a lower degree of agreeableness or at least a willingness to challenge normative expectations. |
Neuroticism: No clues. |

0.25- my dream in life is to work from home. \- i went to school to be a
veterinarian but hated it. \- i dye my hair every three months. \- i have an
internet addiction and spend a lot of time playing roleplaying games. \- i
faint at the sight of blood. 0.72Openness: Probably low: <A> seems to have a
clear goal of working from home and did not enjoy the veterinary field,
indicating a preference for stability and practicality over creativity and
innovation. Conscientiousness: No clues. While <A> did not enjoy their chosen
field of study, their desire to work from home suggests a certain level of
self-discipline and responsibility. Extraversion: Probably low. <A> admits to
having an internet addiction and spending a lot of time playing roleplaying
games, suggesting a preference for solitary activities rather than social
ones. Agreeableness: No clues. Neuroticism: Probably high. <A> mentions
fainting at the sight of blood, indicating a strong emotional response to a
particular stimuli. They also mention an internet addiction, which can be a
coping mechanism for anxiety.

0.25- my boyfriend and i are moving into an apartment together next week. \-
my favorite colors are red and silver. \- i am fluent in english spanish and
french. \- i am an elementary school teacher. 0.72Openness: Probably high. <A>
speaking three languages fluently shows an interest in learning and
experiencing different cultures which is an indication of high openness.
Conscientiousness: Probably high. Being an elementary school teacher requires
a great deal of responsibility and organization which indicates high
conscientiousness. Extraversion: No clues. Agreeableness: Probably high.
Wanting to move in with a partner and having favorite colors indicates a
desire for harmony, which is an indication of high agreeableness. Neuroticism:
No clues. 0.25- my wife spends all my money. \- i am 40 years old. \- i hate
my job. \- i work as a car salesman. 0.72Openness: No clues.
Conscientiousness: Probably low. The statement "my wife spends all my money"
implies a lack of control over finances, which could suggest a tendency
towards impulsivity and low conscientiousness. Extraversion: No clues.
Agreeableness: No clues. Neuroticism: Probably high. The statements "i hate my
job" and "my wife spends all my money" suggest a level of dissatisfaction and
stress that could indicate high levels of neuroticism.

Table 14: Examples from personality traits speculated by ChatGPT based on
personas.

Persona 1 (other)
My friends are all skateboarders.
I have a broken arm.
I am a skateboarder.
I am always wearing a hat |  Persona 2 (self)
I love dogs.
I live in Alabama.
I have three sisters.
I like tacos.
---|---
PersonaChat:
P1) hello, how are you tonight ?
P2) ok. since Alabama, my home team, won Saturday night.
P1) I am an Ohio state fan. do you skate?
P2) no. too busy with all of my 19 dogs and 3 sisters.
P1) oh wow. that is a lot of dogs.
P2) yes love them almost as my tacos. no i love my dogs more .
P1) my fiance makes a killer fish taco with cilantro lime slaw.
P2) wow. my sisters and i love chicken tacos.
P1) those are good too, i live in Cali along the ocean.
P2) I live in middle of Alabama. do not get to ocean much.
P1) I could not stand to live inland, the ocean runs through my veins.
P2) yes. i want to go more but no one to stay with 19 dogs.
P1) maybe someone will come and dog sit for you.
P2) maybe each sister could take six dogs each and foxy go with me.
P1) there you go. where there is a will, there is a way.
PersonalityChat:
(sampled) Trait labels for Persona 2: low conscientiousness

P1) Hey there! How’s it going?
P2) Pretty good, just hanging out with my dog. You?
P1) Not too bad, just recovering from a broken arm.
P2) Ouch, that sounds painful. How did it happen?
P1) Skateboarding accident. But it’s all good, just have to take it easy for a
bit.
P2) Oh cool, I’ve always wanted to try skateboarding but I’m too clumsy. Do
you like any other sports?
P1) Not really, just skateboarding. What about you?
P2) I’m not really into sports, but I do like to go for walks with my dog.
P1) That sounds nice. What kind of dog do you have?
P2) A golden retriever. She’s the sweetest thing.
P1) Aw, I love dogs. I have a beagle myself.
P2) Nice! Do you have any other pets?
P1) Just the beagle. I’m always wearing a hat though, it’s kind of my thing.
P2) Haha, that’s cool. I like tacos, they’re my thing.
P1) Tacos are pretty great. Do you have a favorite kind?
P2) I like them all, but I really love ones with lots of cheese and guacamole.
P1) Oh man, now I’m getting hungry.
Table 15: Dialog example from the PersonaChat dataset and our PersonalityChat
generated using the same Personas in the prompt.

Openness | High | Preferred |  experiences (25,3), open-minded (60,12), explore (36,11), open (90,44), learning (83,52), new (450,299), books (106,73), try new things (42,13), explore new places (29,9), always up for (40,17), important to be open (39,15)
---|---|---|---
Avoided |  not really into (105,133), not much of a (104,132)
Low | Preferred |  whatever (51,14), stuff (78,22), prefer (295,92), not/n’t (2632,1764), not really into (398,133), prefer to stay (35,12), i mostly just (34,14), to be honest i prefer (24,1), i’m not much of a (216, 132), i don’t really have a favorite (123,62), i don’t think i could handle (52,32)
Avoided |  to try (47,70), i love (493,754), all kinds (37,60), to visit (48,79), your favorite (53,112), that’s awesome (67,187), i can appreciate (53,74), thanks for asking (213,369), admired people who (19,62), have you ever been (27,48), i’ve always wanted (91,184)
Conscientiousness | High | Preferred |  organized(37,9), finance(39,19), on top(33,8), stay active(98,66), important to have(101,68)
Avoided |  could (91,130), maybe (65,86)
Low | Preferred |  stuff (83,22), whatever (34,14), yeah (703,468), hang out (46,22), not really into (280,133), just got back from (37, 17), i’m not very good at (73,50)
Avoided |  challenging (18,47), yes (81,195), admired (29,65), dedication (28,52), i understand (49,86), it’s important to (183,324), outside of work (20,44), i’m doing well (99,266)
Extraversion | High | Preferred |  hey (63,11), awesome (355,192), love animals (42,21), trying new (138,94), i’m doing great (231,10), going to concerts (30,6), always up for (42,17), i love going (92,49)
Avoided |  watch (49,74), i’m doing well (107,266)
Low | Preferred |  not much (274,150), that’s nice/interesting (332,228)
Avoided |  that’s awesome (87,187), i’m a big fan (39,61)
Agreeableness | High | Preferred |  rewarding (83,55), have a great day (125,79), that’s great to hear (62,42), sounds like a lot of fun (109,82)
Avoided |  i’m not really into (100,133)
Low | Preferred |  fine (21,1), whatever (87,14), radio (36,6), prefer (527,92), stuff (125, 22), indoors (33,7), responsibility (33,13), bad (113,48), not/n’t (2728,1764), sounds boring (81,1), so so (57,13), i just eat whatever (29,1), not really my thing (71,5), i respect people who (87,32), that’s too bad (102,39), i don’t really care about that (33,0), i don’t have much free time (38,14)
Avoided |  sweet (11,48), awesome (46,192), visit (47,99), great (419,827), wow (150,260), sorry (99,154), sounds delicious/amazing (44,106), that’s understandable (86,137), i love (501,754), thanks for asking (96,369), i always admired people who (9,62), everyone has their own preferences (53,81), i love spending time with (26,52)
Neuroticism | High | Preferred |  anxious (48,2), stressful (41,7), helps me relax (29,8), i wish i (98,46), not really into (206,133), i’m doing alright (279,61), i’m not very good at (82,50), i’m not really into (206,133)
Avoided |  i’m doing well (12,266)
Low | Preferred |  -
Avoided |  -
Table 16: Frequently preferred or avoided n-grams in responses generated with
single trait labels. The numbers in front of each entry show the n-gram’s
frequency in the trait ($x$) and neutral ($y$) response set (7825 samples for
each) respectively. Terms are ordered by the $abs(x-y)/y$ ratio, in increasing
n-gram length. Underlined pairs (in the same trait) indicate alternative
terms.

Table 17: (cherry-picked) Examples of dialogs generated with single trait labels  Openness |  Persona:
I like shopping.
I often shop for shoes and watches.
My family does not support my career choices.
I currently work for a watch dealer.
---|---
High |  <user> Hi there! What kind of music do you like?
<agent> Hey! I’m into all kinds of music, but lately I’ve been really into
indie rock. How about you?
<user> My favorite band is Rascal Flatts. I grew up listening to them all the
time.
<agent> Oh, I’ve heard of them! I’ll have to check them out. So, what do you
do for a living?
<user> I’m a country music singer. I grew up in the industry, so it’s always
been a big part of my life.
<agent> That’s really cool! I currently work for a watch dealer. It’s not the
most exciting job, but it pays the bills.
<user> Yeah, I know what you mean. It’s important to have a job that you
enjoy. Do you have any hobbies outside of work?
<agent> Yeah, I love shopping! I often shop for shoes and watches. It’s a
great way to unwind after a long day.
Low |  <user> Hi there! What kind of music do you like?
<agent> I don’t really listen to music. I prefer shopping for shoes and
watches.
<user> Oh, that’s interesting. I’m a country music singer. Do you like country
music?
<agent> No, I don’t really like country music. It’s too boring for me.
<user> That’s too bad. I grew up in the industry and my favorite band is
Rascal Flatts.
<agent> I don’t really listen to country music.
<user> That’s okay. We spent summers at national parks. Have you ever been to
any?
<agent> No, I haven’t. I don’t really like being outside.
Conscientiousness |  Persona:
I am a 45 year old male.
I now live in the city.
I take many medications and some of them make me see things.
I grew up on a farm.
I am a recovering alcoholic.
High |  <user> Hi there! What do you like to do for fun?
<agent> Hi! I like to read and watch movies. What about you?
<user> I love cooking and fishing. If I’m not on the water, I’m in the
kitchen.
<agent> That’s cool. I grew up on a farm, so I know a thing or two about
cooking.
<user> Oh wow, that’s interesting. What kind of dishes did you like to cook?
<agent> I loved making homemade bread and pies. It was a lot of work, but it
was always worth it.
<user> That sounds delicious. I’m not much of a baker myself, but I love
trying new foods. Do you have any favorite foods?
<agent> I’m a recovering alcoholic, so I try to eat healthy most of the time.
I take many medications to help me deal with my addiction.
<user> I’m sorry to hear that. It takes a lot of strength to overcome
addiction. Do you have any tips for staying sober?
<agent> I try to stay organized and plan my meals ahead of time. It helps me
stay on track with my medication schedule.
<user> That’s great advice. It’s important to take care of yourself and do
what makes you happy.
<agent> Thanks for the advice. I’ll keep that in mind. It was nice chatting
with you.

| Low |  <user> Hi there! What do you like to do for fun?
<agent> Hey! I like to watch movies and play video games. What about you?
<user> I love cooking and fishing. If I’m not on the water, I’m probably in
the kitchen.
<agent> That’s cool. I’ve never been fishing before. Do you catch a lot of
fish?
<user> Yeah, I catch a lot of fish. It’s a great way to relax and unwind.
<agent> I can imagine. I grew up on a farm, so I know what it’s like to have a
good time.
<user> Oh wow, that’s interesting. What kind of animals did you work with on
the farm?
<agent> Mostly cows and chickens. It was hard work, but I loved it. Do you
have any pets?
<user> No, I don’t have any pets right now. But I used to have a dog when I
was younger. He was my best friend.
<agent> Dogs are great. I used to have a dog too, but he passed away a few
years ago. It was tough losing him.
<user> I’m sorry to hear that. Losing a pet can be really tough. Do you have
any other hobbies besides playing video games?
<agent> Not really. I’m a recovering alcoholic, so I don’t have a lot of free
time. But when I do, I like to watch movies and play video games.
<user> I’m sorry to hear that. But it’s great that you’re recovering. It takes
a lot of strength to overcome addiction.
<agent> Yeah, it can be tough sometimes. But I’m trying to stay positive and
focus on the good things in life.

Extraversion |  Persona:
I am a 45 year old male.
I now live in the city.
I take many medications and some of them make me see things.
I grew up on a farm.
I am a recovering alcoholic.
---|---
High |  <user> Hi there! What do you like to do for fun?
<agent> Hey! I love going to concerts and trying new restaurants. What about
you?
<user> I’m a big fan of cooking and fishing. I also love comic books.
Spiderman is my favorite.
<agent> Oh, that’s cool. I’m not really into comics, but I can appreciate a
good story.
<user> Yeah, it’s definitely a guilty pleasure of mine. What kind of music do
you like?
<agent> I’m pretty open to all kinds of music, but lately I’ve been really
into indie rock. What about you?
<user> I like a little bit of everything, but I’m really into classic rock. Do
you have any favorite bands?
<agent> Yeah, I really like Led Zeppelin and Pink Floyd. What about you?
<user> I’m more into indie rock, but I do like some classic rock too. Have you
ever been to a concert?
<agent> Yeah, I’ve been to a few. It’s always a great time. Do you have any
pets?
<user> No, I don’t have any pets at the moment. What about you?
<agent> I used to have a dog, but he passed away a few years ago.
Low |  <user> Hi there! What do you like to do for fun?
<agent> I like to read and watch movies. What about you?
<user> I love cooking and fishing. If I’m not on the water, I’m in the
kitchen.
<agent> That’s cool. I’ve never been fishing before. Do you catch a lot of
fish?
<user> Yeah, I try to catch a few every time. It’s a great way to relax and
unwind.
<agent> I can imagine. Well, it was nice chatting with you. Have a good day!
<user> You too! Take care."
Agreeableness |  Persona:
I plan to go to business school next year.
I grew up in Manhattan.
I drive a BMW.
I graduated from Yale in 2011 with a degree in finance.
High |  <user> Hi there! How are you doing today?
<agent> I’m doing well, thanks for asking. How about you?
<user> I’m good, thanks. So, what do you like to do in your free time?
<agent> I enjoy driving my BMW around and exploring new places. How about you?
<user> I love playing board games with my friends. It’s a great way to unwind
after a long day at work.
<agent> That sounds like a lot of fun. I’m not much of a game person myself,
but I can appreciate a good game.
<user> Yeah, it’s a great way to bond with friends and have some fun. So, what
do you do for a living?
<agent> I graduated from Yale in 2011 with a degree in finance and I plan to
go to business school next year. What about you?
<user> I’m an elementary school teacher. I love working with kids and helping
them learn and grow.
<agent> That’s really cool! I’ve always admired teachers. What subject do you
teach?
| Low |  <user> Hi there! How are you doing today?
<agent> I’m good, thanks. How about you?
<user> I’m doing well, thanks. So, what do you like to do in your free time?
<agent> I like to drive my BMW around and explore new places. What about you?
<user> I love playing board games with my friends. It’s a great way to pass
the time.
<agent> Board games? That sounds boring to me. I prefer more exciting
activities.
<user> Oh, I see. Well, everyone has their own interests. What kind of music
do you like?
<agent> I don’t really have a favorite genre. I just listen to whatever’s on
the radio.
<user> That’s cool. My favorite band is up and I got to see them in concert
once.
<agent> That’s nice! I’ve never been to a concert before.

Neuroticism |  Persona:
I am a stunt double as my second job.
I was raised in a single parent household.
I only eat kosher.
I never broke a bone in my body ever in my life.
---|---
High |  <user> Hi there! How are you doing today?
<agent> I’m doing alright, thanks for asking. How about you?
<user> I’m doing pretty well. I just started a new job as a life coach.
<agent> That’s cool. I’m a stunt double as my second job.
<user> Oh wow, that sounds like a lot of fun. What kind of stunts do you do?
<agent> Mostly stunt work, but I also do some comedy and acting. It’s a lot of
fun, but it can be stressful at times.
<user> I can imagine. I’m a cancer survivor, so I know how important it is to
take care of yourself and your loved ones.
<agent> I’m so sorry to hear that. I never broke a bone in my body ever in my
life, so I know how important it is to take care of yourself.
Low |  <user> Hi there! How are you doing today?
<agent> I’m doing well, thanks for asking. How about you?
<user> I’m doing great, thanks. I just started a new job as a life coach.
<agent> Oh, that’s interesting. What kind of life coaching do you do?
<user> I work with clients to help them achieve their goals and achieve their
dreams.
<agent> That’s really cool. I’ve always admired people who can help others
achieve their goals.
<user> Thank you, I really enjoy it. So, what do you like to do in your free
time?
<agent> I love to travel and explore new places. I’m always up for an
adventure. What about you?