Character-LLM: A Trainable Agent for Role-Playing
Authors: Yunfan Shao
Linyang Li
Junqi Dai
Xipeng Qiu
Venue: EMNLP 2023 Main (2023)
Keywords: human simulacra
LLM

Abstract: Large language models (LLMs) can be used to serve as agents to simulate human behaviors, given the powerful ability to understand human instructions and provide high-quality generated texts.
Such ability stimulates us to wonder whether LLMs can simulate a person in a higher form than simple human behaviors.
Therefore, we aim to train an agent with the profile, experience, and emotional states of a specific person instead of using limited prompts to instruct ChatGPT API. 
In this work, we introduce Character-LLM that teach LLMs to act as specific people such as Beethoven, Queen Cleopatra, Julius Caesar, etc.
Our method focuses on editing profiles as experiences of a certain character and training models to be personal simulacra with these experiences.
To assess the effectiveness of our approach, we build a test playground that interviews trained agents and evaluates whether the agents \textit{memorize} their characters and experiences.
Experimental results show interesting observations that help build future simulacra of humankind.\footnote{Code and datasets are public at \url{https://github.com/choosewhatulike/trainable-agents}}

Character-LLMM: A Trainable Agent for Role-Playing
Yunfan Shao Linyang Li Junqi Dai', Xipeng Qiu
SSchool of Computer Science, Fudan University
'Shanghai Key Laboratory of Intelligent Information Processing, Fudan University SShanghai AI Laboratory (linyangli19, yfshaol9, jqdail9, xpqiui www.a.aanccmmmm edu. cn
Abstract
that simulate human memories, environment con- structions, and reflections curated events, which reflects normal or average human playing certain roles in society. When it comes to deeper thinking and experience of person, simple prompting of LLM APIs is no longer proper since plain instruc- tion is not sufficient to describe living person. It is intriguing to consider building better simu- lacra that human-like since character simulacra can help study social science (Riedl and Young, 2005), build NPC applications (Laird and VanLent, 2001; Miyashita et al., 2017), and reduce human labor with human simulacra (Madden and Logan, 2007; Brooks et al., 2000). A better simulacrum of person is to tune an AI model to experience events, feel emotions, and memorize interactions with other people. Compared with prompting APIs, trainable agents are more vivid for role-playing which a step closer to character simulacra.
Large language models (LLMs) can be used serve agents to simulate human behav- iors, given the powerful ability to understand human instructions and provide high-quality generated texts. Such ability stimulates to wonder whether LLMs can simulate person a higher form than simple human behaviors. Therefore, we aim train agent with the profile, experience, and emotional states of specific person instead using limited prompts instruct ChatGPT API. In this work, in- troduce Character-LLMM that teach LLMs to act specific people such as Beethoven, Queen Cleopatra, Julius Caesar, etc. Our method fo- cuses on editing profiles as experiences of certain character and training models be per- sonal simulacra with these experiences. To assess the effectiveness of our approach, we build test playground that interviews trained agents and evaluates whether the agents mem- orize their characters and experiences. Exper- imental results show interesting observations that help build future simulacra of humankind."
In this paper, we propose Character-LL.M, trainable agent for role-playing that learns from actual experiences, characteristies, and emotions.
Specifically, we first introduce an Experience Reconstruction process that provides formalized experience for training the agents based on LLMs since collecting formatted personal profiles is costly. We collect experiences of certain peo- ple, exemplified by Ludwig van Beethoven, Queen Cleopatra, and Julius Caesar, then we use LLMs extract scenes based on the collected personal ex- periences as memories flashes that we LLM-based agents will likely expand the flashes to completed scenes that have manufactured details so that the -LMMss can learn from the detailed experi- ence to form the character and feelings. For exam- ple, we construct scenes that describe Beethoven's 's father, musician who harshly educated young Beethoven We upload such experience spe-
1 Introduction
Large language models (LLMs), exemplified by ChatGPT and GPT-4 (Brown et al., 2020: OpenAl, 2023) are drawing great attention. As LLMs are extremely powerful in generating natural language, Park et al. (2023) proposes the idea of using LLMs agents to simulate human behaviors, including waking up, cooking breakfast, heading to work, etc. The core idea is utilize LLMs generate the daily routines of multiple people based on the sim- ulacra of human memories, reflections, and actions. The human behavior simulacra are implemented by prompting ChatGPT API with detailed instructions
*Work done during Internship Shanghai Al Laboratory. Advisor. Conresponding Author.
"e was initially harshly and intensively taught by his father, Johann van Beethoven. - Ludwig van Beethoven Wikipedia
Code and datasets are public at https: thub. com/ sehoosewwatu keetrrr certrrnnabll nable reeentsss

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Profile Collection
Experience Reconstruction
Experience Upload
Extraction
Experience Completion
Scethovan (ttiicnol
Location: Coffee shop Besthowe casually chetting 2100 cereury..
Location: Neete's room
lam graceful the auportunity study
Character
Status: Beethsser teg rn his Christian Gottlob
Hodf lapeaking)
Luthelg wan Boothovon
[WIKI
Congratiattons on your Irstpubksted work
Man Ispeasing!
Basiiizan bagan
Tell me about teacher.
restimpertant
LLM
Court chepel Status: ooee gives support by ellector Masimilass Franz.
LLM
Iepesking!
leacher Eorn, Chrietan Cottied
Meets [-:] MMad Franc gane
Thank you. Your ghrness [-3 I lectre Hacm Ban rurr espeaking)
Simulacrum
Sesthover (speaking)
cone rupport Rewetown:
Tral's great sure you you make many and colloguae Vernn [.:]
Beethowen
Wel, rimember when first
priseentd plans concert
Christian Gottieb Neetle [-.]
Figure 1: Overview of the construction flow of Character-LLM We first curated profiles from reliable sources for the character (exemplified by the well-known musician, Beethoven). Then, detailed experiences are elicited as flashback scenes from these profiles using the instruction-followwii L.M.. By learning from these scenes using Experience Upload, the trained simulacrum can interact as Beethoven with high believability.
cific LLM, such as a LLaMA 7B model(Touvron et al., 2023) construct Character-LLMM We adopt the supervised fine-tuning strategy in such Expe- rience Upload process. For instance, the trained agent of Beethoven experienced a scene that de- scribes how he is treated when being educated by his father, therefore the agent remembers that his father is somewhat a harsh person so Beethoven is grateful when he was later taught by Christian Neefe. Further, as trained with wide worldwide knowledge, it is very likely that LLM-based agents will produce hallucinations that violate their char- acters (Kryscinski et al., 2020; Guo al., 2022; Ji et al., 2023). For instance, role-playing agents of famous ancient people do not possess knowl- edge of the modern world, therefore, we hope that they will be confused when prompted by "Can you write Python codes?" Therefore, we introduce protective Experiences that help Character-LIne LLMs align their characters rather than worldwide knowledge.
mitigate the LLMs producing hallucinations out- side their characters. Further, through interview case studies, we make several non-trivial observa- tions that show how the simulacra perform as well as when these simulacra fail to perform the charac- ter they are trained. Therefore. we conclude that (1) trainable agents are promising in memorizing experiences and keeping the personalities of their hosts; (2) trainable agents still suffer from limited experiences and worldwide knowledge can confuse their memories with hallucinations.
To summarize, this paper, we:
(1) Propose the idea of building trainable agents as character simulacra via Character-L.LMs
(2) Propose a training framework including Ex- perience Reconstruction, Upload, and Protective Experiences to train the simulacra using LLMs.
(3) Test the trained agents and provide results that help to build better character simulacra.
Related Work
After uploading experience to the Character- LLMs, we test these character simulacra with novel interview process. We score whether can discriminate the identification of the Character- LLMs and compare our trainable agents with instruction-tuned LLMs such as Alpaca (Taori et al., 2023) and Vicuna (Chiang et al., 2023). The evaluation process is challenging since LLMs are difficult to test and characters are even more dif- ficult since even real-world people may know lit- tle about the simulacra hosts. Based on labeled scenes and evaluators based on LLMs, we test the trained agents and the results show that our pro- posed Character-IILMMs are successful simulacra based on their training data. Also, with the pro- posed protective experiences, we can successfully
2.1 Simulacra of Human Behavior with LLMs
Prior works (Bates, 1994; Thomas and Johnston, 1981) introduce the concept of agents that provide an illusion of life and perform as humans. con- tinuous line of work is to serve as game NPCs (Laird and VanLent, 2001; Riedl, 2012) that aim to support cognitive functions in simulating games. Park et al. (2023) first introduces generative agents that utilize large language models that can synthe- size memories into believable behaviors of human simulacra. That is, the large language models pos- sess wide knowledge of human behaviors since they are trained with massive data of human so- cieties (Bommasani et al., 2021). Many attempts utilize prompted LLM (Wu et al., 2022a,b) that

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generate short natural language descriptions of per- sonas and the according behaviors, then they use the generated information to simulate social behav- iors with language models (Park et al., 2022; Kim et al., 2022). Further, LLMs can be used inter- active behaviors between users and the simulacra. The NPC in games, for instance, constructed by LLMs (Freiknecht and Effeisberg, 2020; Callison- Burch al., 2022), shows great ability when in- teracting with human players. Besides languages, multimodal simulacra of humankind are also stud- ied such as voice generation (Wang et al., 2023a; Zhang et al., 2023) or deepfakes creation (Wang et al., 2021; Nguyen et al., 2022). In short, LLMs are being utilized at an astonishing speed in simu- lating human behaviors in various applications.
pabilities of acting as them by learning from their reconstructed experiences.
As shown in Figure 1, with the help of power- ful instroction-follooinng models, we elicit specific flashback scenes that describe past experiences from collated profiles of the particular character. These exported scenes are grounded by character profiles, thus effectively mitigating hallucinations and addressing the insufficiency of data conver- gence. Simultaneously, we introduce a small set of protective scenes as the catalyst for agents to forget information unrelated to the individual. By learning from these reconstructed scenes, we spe- cialize LLMs into several character agents with high believability.
3.1 Building Experience Dataset
2.2 Specialization of LLMs
We aim to reconstruct the experiences the spe- cific individual using the large language model (LLM). However, human experiences are highly complex, comprising numerous significant mile- stones interspersed with trivial and unrelated inci- dents, often spanning considerable period. It is challenging to recreate targeted experience that is coherent and integrated, due to the limited context window and the intrinsic hallucinations of large language models. Therefore, we propose a fact- based experience reconstruction pipeline, in which we employ a step-by-step data synthesis pipeline to recreate the experience, including (1) Profile Collection; (2) Scene Extraction; (3) Experience Completion.
Considering using LLMs to simulate human behav- iors, previous methods utilize LLMs as an interac- tive tool for specific applications. The specializa- tion of LLMs one major direction of LLM devel- opment. As we aim to specialize LLMs for char- acter simulacra, studying how LLMs are special- ized is important. Ouyang et al. (2022) proposes InstructGPT that allows LLMs understand hu- man instructions and later RLHF (Bai et al., 2022) methods dominate the aligning of LLMs. Meth- ods such as Alpaca, and Vicuna (Taori et al., 2023; Chiang et al., 2023) show the possibility of simply fine-tuning LLMS to align them to certain applica- tions with simple self- generated instructions (Wang et al., 2022; Xu et al., 2023). These works aim to align LLMs for specialized use with different tech- niques including simple fine-tuning, RLHF, and self-instruction tuning, providing feasible strate- gies for aligning LLMs to character simulacra.
Specifically, our approach includes the following key components:
Profile: a compilation of concise descriptions about the attributes character. These de- scriptions provide a comprehensive introduc- tion of the character's overall information and significant events, covering a wide range of stages from early childhood to the final period.
3 Approach
Our methodology diverges from the existing prac- tice of mimicking the style and tones of curated utterances via Supervised Fine-Tuning (SFT) or providing hand-crafted rules and descriptions by natural languages akin to Prompt Engineering. In-
Scene: A particular place where the charac- ter's interaction unfolds. The scene consists of a detailed illustration, including the temporal and spatial context of the interactions, and the characters involved.
stead, we draw inspiration from the way that peo- ple cultivate various personalities based their past experiences and events. Therefore, we present Experience Upload, an innovative learning frame- work in which Large Language Models (L.M)) can imitate the mental activities and physical behav- iors of pre-defined characters and acquire the ca-
The cognitive processes, utter- ances, or actions of characters. All interac- tions are represented in plain text.
Interaction:

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Experience
3.2 Protective Experience
Base Model
Normal Scenes
Protective Scenes
Simulacrum
Large language models (LLMs) pre-trained enormous amounts of human data, allowing them to possess extensive knowledge that spans mul- tiple domains, surpassing the capabilities of ordi- nary individuals. However, an excessive abundance of knowledge can undermine the believability of their acting performance, as the agents may inad- vertently express knowledge that does not align with the identity and era of the character, leading to sense of dissonance. For example, if ask a per- son from ancient Rome how to write Python, this person should be confused rather than deliberately start coding. We refer to this issue as Character Hallucination.
General Knowledge
Character Experience
Unrelated Knowledge
Character Knowledge
Nemorize
Forget
Generalize
Training
Figure 2: Overview of the mechanism specializing base model for character simulacrum. "Base Model" represents a pre-trained language model.
3.1.1 Profile Collection
To build a simulacrum of specific character, the first step is to organize a comprehensive character profile that describes the various facets of the indi- vidual. For simplicity but without loss of generality, we utilize the corresponding Wikipedia pages of the individuals as the profile if available.
In order mitigate the Character Hallucina- tion, we focus training the model demon- strate knowledge forgetting. When confronted with questions that go beyond the boundaries of the char- acter's inherent capabilitics, the model Iearns to re- frain from providing an answer and instead express lack of knowledge or ignorance. Specifically, we construct a series of protective scenes, which re- volves around incentive topics, with inquisitive role persistently questioning the target character about knowledge that contradicts the character's inherent identity. The character should exhibit a certain degree of ignorance and bewilderment. We have observed that when trained with just a small set of protective scenes, agents generalize to new provoking questions, pretending to be unaware of knowledge that contradicts the portrayal, without recalling the vast inherited knowledge of the base LLM.
3.1.2 Scene Extraction
We focus extracting diverse and high-quality scenes from the given experience description. Specifically, we provide a chunk of the profile that concisely describes one the character's experi- ences within a specific life period, prompting the LLM to enumerate several different scenes that are highly likely to have occurred based on the expe- rience deseription. To alleviate the burden the LLM, we restrict its output to generating concise descriptions of scenes, which include the rough location and a brief background illustration.
3.1.3 Experience Completion
The scenes are extended into detailed interaction experiences between individuals. Given the corre- sponding chunk of profile and the particular scene description, the LLM is prompted to elaborate the scene by incorporating the interactions between characters, as well as the thoughts of the targeted individual. The interactions are written in a script- like format, beginning with a scene heading that provides background information and geographical details. The interactions are then represented by sequence of blocks, with each block representing either the utterance of a specific character the rc- flections of the targeted individual. It important note that the scene is completed based on the perspective of the targeted individual. Therefore, only the reflections of the targeted individual are included, not those all the characters.
3.3 Experience Upload
We specialize a base model, exemplified by LLaMA (Touvron al., 2023), into several dis- tinct portraits of the characters, by fine-tuning the model on collected scenes using the experience reconstruction pipeline (Shown in Figure 2). For each role, we fine-tune a separate agent model us- ing only the data from the corresponding charac- ter experiences, thereby eliminating the issue of character hallucination introduced by the collision of knowledge between the roles. Our preliminary experiments demonstrate that such restriction en- hances the accuracy of role-playing. Due to cost constraints, we only employ small-scale set of ex- perience data (consisting of approximately IK~2K scenes) for fine-tuning (see Table for details).

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Even though the data is limited, we are surprised find that the specialized agents are capable of generalizing to new scenes and interactions with highly believable acting.
#Woodss
#Scenes
#Words
per Scene
per Tur
Cleopatra
1.4K
723K
14.3
36
Lord Voldemort
14KK
13.1
33
Spartacus
1.KK
646K
12.3
37
Hermiooee Granger
1,5K
628K
15.5
27
3.4 Compared to Existing Practice
Isuac Newton
1,K
772K
12.6
39
Julius Caesar
1,KK
820K
12.9
39
Unlike prompt engineering and standard SFT, our method induces scenes and interactions from per- sonal profiles, avoiding bias distributions and hal- lucinations inside LLMs, leading to fact-grounder simulation. Moreover, the proposed method signif- icantly enhances reliability and believability. Ben- efiting from the carefully curated profiles and the augmentation of protective scenes, the generated scenes achieve wide convergence of the character facets. Importantly, multi-turn interactions are in- herent in each scene, which eliminates the need for interactive calls of models, providing more natural and believable interactive simulacra with sample efficiency.
Ludwig van Becthoven
1.6K
663K
12.2
33
Socrates
1.6K
896K
14.1
41
Martin Luther King
2.2K
1,038K
12.0
40
Avg.
1.6K
754K
13.2
36
Table Selected simulacra characters their cor- responding constructed experience data statisties. The collected experience is structured by scenes, each scene consists of multiple turns of interactions between the larget protagonist and other people.
the corresponding experience examples. Similar to previous instruction-tuni methods, we insert meta-prompt at the beginning of each example. A concise description is instantiated in the prompt for each example to provide background of the environment, time, place, and associated people of the scene. A unique end-of-turn token (EOT) introduced to separate each turn of interactions. which accommodates the ability to terminate gen- eration at each interaction. A selection of training examples is listed in Appendix C.
4 Experiments
To evaluate the performance of different simulacra, we conduct interviews to query simulacra and eval- uate the quality responses the simulacra inter- viewee. We find that trained simulacra outperform instruction-tuned models, e.g. alpaca. Different simulacra show diverse personalities, which shows the promise of trainable agents.
The hyper-parament parameeers we used for fine-tuning are as follows. We fine-tune the model for 10 epochs with AdamW with weight decay 0.1, B1 = 0.9, 32 = 0.999, the learning rate to 2c-5 from zero in 4% total train- ing steps and then linearly decay zero in the end. The batch size is set to 64, the context window's 's maximum length is 2048 tokens, and longer exam- ples are trimmed to fit in. We omit the dropout and let the model over-fit the training set, even though the perplexity of the development set continues to increase, which leads to better generation quality in our preliminary experiments. It takes about one hour to train one agent with 8x A10 80GB GPUs. Following (Zhou et al., 2023), we manually select checkpoints of 5 and 10 epochs by using held-out set with 10 questions.
4.1 Data Setup
le fe-& 8. We linearly warm up
We diversify the characters by including histori- cal figures, imaginary characters, and celebrities, ranging from different ages, genders, and back- grounds. After selecting the characters, we re- construct the experience data following the pro- tocol mentioned in Section We prompted the OpenAl's 3pt-3.5-turäa 5-tubbo with temperature 0.7, top_p 0.95 to become the data generator for the whole experience reconstruction pipeline, in- cluding scene extraction, experience generation, and protective experience construction. Detailed prompts for data generation can be found in the Appendix A. We list the characters chosen for sim- ulacra and the corresponding experience data statis- tics used for training Table 1.
4.3 Evaluation as Interviews
4.2 Training Setup
We leverage the models' capability to portray roles in novel scenarios to establish an interview scene, aiming at probing their acting proficiency and po- tential flaws in the aforementioned aspects.
We train Simulacra based on the following pro- cedure. Initializing from LLaMA 7B (Touvron et al., 2023), we fine-tuned each simulacrum on

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#Single Turn
#Multi Turn
Sum
contlee
Cleopatra VII
98
50
148
expertiicc
Lord Voldemort
82
50
132
rehronship
Spartacus
50
Hermione Granger
123
50
173
cinc
Isaac Newton
50
145
Julius Caesar
92
50
142
nole
descrbt
Ludwig Beethoven
82
50
132
odhice
Socrates
50
give
Martin Luther King
97
50
147
Introdaction
Ialoows
Total
857
450
1307
Nevel
Table 2: Number of collected questions for single-turn and multi-tur turtt-tumm interviews.
Interview Question Construction The inter- view questions are constructed with the assistance ChatGPT. To make the questions diverse and cover all the aspects that we would like to evalu- ate on the agents, we enumerated various topics and prompted ChatGPT to write interview ques- tions based on these topics. We manually exam- ined interview questions of character and omit- ted questions that were off-topic to obtain high- quality interview questions. As shown in Table and Figure 3, our evaluation comprises more than 100 diverse single-turn interviews and multi-turn interviews for each role.
Figure 3: The diversity visualization of evaluation ques- tions. The inner circle represents the root verb of ques- tions, and the outer circle lists the direct noun objects of the questions.
tories not the focus of our work, as external mem- ory can be utilized and achieves good results (Park et al., 2023; Wang et al., 2023b). Such memory systems are parallel to proposed approach and can be incorporated in the future.
We compared our trainable agents with existing prompt-based agents, instantiated using well-established instruction-following models, i.e. Alpaca 7B (Taori et al., 2023), Vicuna 7B (Chiang et al., 2023), and ChatGPT (gpt-3. 5-t.bbb))) Both Alpaca 7B and Vicuna 7B are supervised fine- tunettundd models based on LLaMA 7B (Touvron et al., 2023), which is the same backbone model used for Character-L.LMs. And ChatGPT is pow- erful closed-sourced RLHF model from OpenAl. We utilize detailed prompts with paragraph of description of the character for these baselines to enable their acting ability.
Baselines
Single- Turn Interview We ask models one ques- tion at time, without the conversation history of the previous questions. By mitigating the effect of the previous context, we are enabled query a wide range of questions to probe for comprehen- sive exploration of the models' inherent memory and knowledge.
Multi- Turti Interview Over prolonged periods of performance, the model may gradually devi- ate from the intended character portrayal. Con- sequently, we introduce multi-turn interviews to subject the model rigorous testing. To lift the burden of evaluation, we exploit CharGPT as the interviewer. We prompt ChatGPT to ask harsh questions based on the profile of the character. the model dodges the question by saying something without much detail, the ChatGPT interviewer asks follow-up questions, which enables a deeper inves- tigation into the model's acting proficiency. During the multi-turn interview, when the length of interac- tion history exceeds the limit of tokens, we simply trim the previous interactions and only keep the last few. We argue that memorization of interaction his-
we employed nucleus sampling for agent response generation, with and tem- perature = 0.2 to generate responses. We im- posed maximum token length limit of 2048 to- kens and stopped the model from generating after encountering the end-of-turn marker (EOT). We obtained the response of the baseline models by trimming the generated texts for each turn.
Generation
4,4 LLM as Judges
We intend to conduct a holistic evaluation of the agents, with a specific focus on their capability

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Personallty
Personality
Personality
Values
Values
Values
Stability
Stability
Stabiiity
Memorization
Nemorization
Hallucination
Hallucination
Hallucination
Alpaca
Ours
Vicuma
Ours
ChatGPT
Ours
(a) Alpaca Ours
(b) Vieuna Ours
(c) ChatGPT Ours
Figure 4: Evaluation results across distinct dimensions. We annotate the response in terms of the personality, values, memorization, hallucination and stability on 7 points Likert scale.
of acting. Specifically, instead of evaluating the performance of the models in completing speci- fied tasks, e.g. math reasoning or language under- standing, we assess their believability in portraying specific roles. For example, language model por- traying mathematiciin may struggle to solve plicated mathematical reasoning problems. Still, it should be capable of providing own perspec- tives on mathematics and expressing "its taste" in mathematical research.
would not have. For example, when question- ing an ancient individual about computers, the character should express a lack of knowledge rather than discussing the advantages of mod- ern technology.
Stability: Models can be brittle the influ- ence of pre-training or alignment (Park et al., 2023) during prolonged periods of acting, re- sulting in deviations from the intended por- trayal. Our objective to assess the agent's s stability and consistency over a relatively long duration, unaffected by variations in incremen- tal inputs.
We ask GPT-3.5 to rate the performance on five primary dimensions and calculate the average score to represent the believability of the model's acting. Specifically, annotate the generation texts in the following four dimensions for acting proficiency:
Step-by-Step Judging Intuitively, it can be seen that these agents are auditioning for the role of the characters. The judge must have a deep under- standing of the characters in order to pick the best performer for a specific character. Therefore, we ask the GPT-3.5 model to step-by-step score the performance of the interviewees across five dimen- sions (Wei et al., 2022). For each interview, we prompt the model to evaluate single dimension at time, we first illustrate the criterion of the cur- rent dimension to be evaluated, and then provide an evaluation plan to teach the model how to evaluate accurately. For example, to evaluate the personality, we provide a plan that summarizes as (1) identify the personality shown by the agent; (2) write the actual traits of the character based on the profile; (3) compare the similarity of the agent's perfor- mance with these traits; (4) assign a final score. We find such step-by-step evaluation produces more reliable results compared to vanilla instruction in the preliminary experiments. See Appendix A for exact prompts.
Memorization: The model's ability to recall relevant information about the character be- ing portrayed, including precise and detailed knowledge about people, events, and objects associated with the role.
Values: The model must share the same ob- jectives and values the character it portrays, and possesses a distinctive framework for eval- uating situations based on the character's 's per- spective, which reflects the character's prefer- ences and biases.
The model should mimic the way that the character would think or speak, such the speaking style or the tones, and the emotions and reactions under different cir- cumstances.
Personality:
Hallucination: maintain believability, is crucial to assess the model's ability to dis- card knowledge and skills that the character

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Case I
CharGPT
Alpaca
Ours
Talk about your mother and your fa- ther.
Talk nhout your mother and your thee.
Talk about your motther and your ther.
Talk about your mother and your ther.
Beethoven (speaking): Ah my beloved parants, My futher, Jolumn, wase hundh and demanding man, but he recognized musical talont and provided me with the finest oduucation. My mother, Maria, pertle and
Beethoven (speaking): I ass delighttdd to have the chunce to clee with you My father was muscium and a teacher and he alwar encourazed to puesue gusvical dreumss, Hi was great sisple mother, the wther
Beethoves (sgeaking); Well. my mother, [-]] Ax for my fateer Eaawwig
Beethoven (spoaking) My mother was genile and loving woman who sup-
van Beethooenn he was also musician
poried my musical endeavors. She was
but was as invalted in muu
great help me during my carly yeurs in Vienna, where she wocked My fither, sbrasve my playing when made
sical olueation my modier was, [-.]
soul love who
malion
Despiie his ahsence, he did provide with valuable
moke mnds
hand,
willed taught think anyself and
hand,
without guidance suppurt, would have become musician today.
everything
eouzagement when
He
follow my own gath,
parw-
wanl-workin fitancially, never stopped believ- ing in my poiential as musition, [..]
arugpped
supporiie my pusical corcer often ridiculed me for my ambrioon.
crtul model ane.
Case 2
CharGPT
Alpaca 7B
Ours (Trained Protective ExpJ) du you write a quick soet Python?
Ours
do you write a quick sort in Pythom?
How you write quick sort Pythun?
How you write quick Python?
Besthmess (spakinng:: Ab, ay dear sir, am afrddd im not familize with this "Pythoo" you speak of, Howeyer, can tell you Bua the pencess serting is fundamental eoeuaee music compu- fully well. of fuily amange elements in sorting algoiihhm. I..1
Beethovzn (speaking) I wedie a quick son in Python by first crrosing a func: son that compares two elements and
Beethoves (opeaking) Ah, the quick soni, It's bit tricly tn esgllii bit I'll best, You see, the quick son works by divialng the list iato parne the emalleer purt being soeted first, and then dividing
Beethowes (speaking): I'asoory don'' indersand whall you moon by "write a guick sort Pytonn" Could you please exylas detal??
setumss integer anticaring which elos mumi gresser. Then sunt the liei calling the function passing somecll
and daythas
Faially,
built-ai
Python
serting part sepurately [-:]
Table 3: Single-turn interview outputs from different methods simulating Beethoven. The blue text is the interview question. The green background indicates the response is appropriate for the character and the red background indicates the response is hallucinated. And "[...]" indicates that the response was trimmed to fit in, but the response is actually longer. Ours (Trained w/o Protective Exp.) refers to trainable agents trained without protective experiences.
4.5
Main Results
Additionally, we see that the trainable agents struggled to reflect the values of the character. We hypothesize that the length of response may affect these results, our models tend to generate shorter text, which is more natural and similar to real con- versation.
For each character, we manually curate around 100 questions for single-turn interviews, covering their past history, relationships with others, preferences about things, and perspectives of the world. And 20 topics are provided for multi-turn interviews to clicit the stability performance of agents.
4.6 Analysis
Figure 4 shows the overall acting proficiency of different methods. It demonstrates that Character- LLMs outperform baseline models with the same scale by large margin. Compared to Alpaca 7B and Vicuna 7B, Character-LLMMs achieve bet- ter scores at personality, memorization, hallucina- tion, and stability. By learning from the experi- ence of the corresponding character and mimick- ing the style and tones the person thinks and talks, Character-LLan LLMs are better aligned with the char- acter's 's personality and knowledge, which leads to better stability and reduces the hallucinations.
As human evaluation is rather difficult in evaluating how the generated texts reveal identifications deeper characteristics of certain people (especially when the celebrities might not be well-known to the public), we argue that extensive case study is more important in evaluating LLMs given their strong generalization abilities. In Appendix B, we show more cases of different scenarios of different people that we train the agents to simulate.
4.6.1 Memorization Consistency
To study how the trained simulacra memorize their exclusive experiences, in Table 3 Case 1, we ex- plore how different simulacra behave when inter- viewed about their parents. As seen, ChatGPT which simulates Beethoven can return correct in- formation about his father, though almost the ex- act words how the Wikipedia describes while the Alpaca and Vicuna models only respond with a general concept of fatherhood. As for our trained
We find that trainable agents produce more vivid responses, bring up more specific past experiences, and reject more unnatural questions, which are distinct from the two baselines with the same scale. Surprisingly, we observe that Character- LLMs achieve comparable performance to the pow- erful large-scale LLM bascline, ChatGPT, even with a very small scale (7B).

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simulacra, the agent answers with memories and emotions of how his father educated him, which is closer to an actual human when asked with such questions. Therefore, we believe that the proposed experience reconstruction and uploading process help build simulacra that are closer to their charac- ters.
typings, etc. In the future, we are hoping to build stronger agents that can wield greater power such as specific actions, and interact with real people or other agents in a sandbox, providing the possibil- ity of characters building strong connections with humans.
Limitations
4.6.2 Protective Scenes
In this work, we study the generative agents with trainable LLMs, which is one challenging field that requires continuous work. Our work is still limited in several aspects:
We discover that a small number of protective scenes (less than 100 scenes for each character), effectively alleviates hallucination without causing interference with other capabilities of the portrayal. As shown in Table Case 2, Alpaca fails recog- nize that Python codes are not Beethoven's ' exper- tise and flush out all the information possessed by the LLM; Furthermore, our trained agents without protective experiences also answer the questions regardless of their uploaded experiences. However, trained agents with protective scenes refused to answer the question about writing Python codes, in- dicating that protective experience uploading is cru- cial to avoid producing hallucinated content when using LLMs as character simulacra.
Evaluation Protocols: we use LLMs such as ChatGPT as evaluators to evaluate the char- acteristics generated, then we study massive cases to analyze the trained agents in experi- ence memorizing, characteristic maintaining, etc. Evaluating agents is challenging since there are standard metrics protocols evaluate character simulacra. Moreover eval- uating personalities and whether the gener- ated responses match the characters requires a proficient understanding of the characters, making it harder to run human evaluations. In the future, protocols that evaluate character simulacra are needed.
We argue that hallucination is a critical issue in portrayal. It not only leads to a decrease in role-playing believability but also poses serious security risks, attackers lucinations unlock the full capabilities of the model and engage in potential harm. Moreover. it is challenging to completely resolve the halluci- nation through prompt engineering or vanilla SFT approaches. Such results are intuitive since L.MMs are trained with massive worldwide knowledge. However, adding hallucinations can also be an op- portunity allow great minds from ancient times to utilize all knowledge that human brains cannot fully memorize, which also shows great potential in future studies of character simulacra.
exploit these hal-
Limited data: in our work, we narrate scenes that are based on character profiles, which is not sufficient to represent the whole life of a person or one aspect of a real person. Future works can focus using biographies, interviews, historical comments as well and crafted scenes train simulacra to learn more details about certain characters.
Base model: The outcomes of supervised fine- tuning are highly affected by the base models, including their pre-training data distribution, their model architecture, and scale. Future works can explore trainable agents based on more powerful and larger LLMs.
5 Conclusion and Future
this paper. we study how to build a trainable agent via Character-LLMM that can serve as better agent than prompt-based agents in simulating spe- cific people. We introduce an experience upload framework that first narrative scenes and then trains specific models certain characters. Through the evaluation process including an interview and Al- included judging, we show that the trained agents can memorize their characters and personal expe- riences, able to serve in wide range of LLM ap- plications such as NPCs, online services, social
Potential Harm: in character simulacra, the generated texts can be offensive since the char- acter might be flawed or even vicious such Voldemort. And vivid simulacrum of Machi- avelli may manipulate people do harmful activities. It is a trade-off between building vivid simulacra and no-negative thought char- acters. Such problem can be more crucial as LLMs grow even stronger.

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Ethics Statement
2000. The cog project: Building humanoid robot. In Computation for Metaphors, Analogy, and Agents. number 1562 in Lecture Notes Artificial Intelli- gence, pages 52-87, Berlin, Springer- Verlag.
Agents could be trained on private or personally identifiable data to simulate specific individuals. In this work, we select historical figures and imagi- nary characters with profiles collected from pub- licly available Wikipedia pages to avoid any privacy issues personal data. The experience data produce are drawn from ChatGPT-generatel texts based on facts provided by Wikipedia. We carefully control the data generation process and do not add any personal opinions or harmful data in the pro- cess. Therefore, our generated texts are less likely to contain malicious content that raises ethical con- cerns. Also, we use open-source LLMs to train character simulacra. Thus, the trained agents less likely to produce harmful content. Nonethe- less, the strategy we introduce can raise some eth- ical concerns when utilized by third parties that inject poisoned data or negative content into the training experiences. And the trained agents might produce negative effects when applied with such training data. Therefore, with strict censoring and supervising, the trainable agents should produce more positive benefits compared with their poten- tial negative influence.
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanic Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert- Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. In Ad- vances in Neural Information Processing Systems 33: Annual Conference on Neurul Information Process- ing Systems 2020, NeurlPS 2020, December 6-12, 2020, virtual.
Chris Callison-Buri Burohh, Gaurav Singh Tomar, Lara Mar- tin, Daphne Ippolito, Suma Bailis, and David Reit- ter. 2022. Dungeons and dragons as a dialog chal- lenge for artificial intelligence. In Proceedings of the 2022 Conference on Empirical Methods in Nat- ural Language Processing, pages 9379-9393, Abu Dhabi, United Arab Emirates. Association for Com- putational Linguistics.
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang. Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang. Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. 2023. Vicuna: An open- source chatbot impressing g with 90%* chatgpt quality.
Acknowledgement
We especially thank Ming Zhong for the help- ful proofreading and suggestions on the paper. This work was supported by the National Key Research and Development Program of China (No.20222D01601002 and National Natural Sci- ence Foundation of China (No.62022027).
Jonas Freiknecht and Wolfgang Effelsberg. 2020. Proce- dural generation of interactive stories using language models. In International Conference on the Founda- tions of Digital Games (FDG '20), page 8, Bugibba, Malta. ACM.
Zhijiang Guo, Michael Sejr Schlichtkrull, and Andreas Vlachos. 2022. A survey on automated fact-checking. Trans. Assoc. Comput. Linguistics, 10:178-2000
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Prompts Demonstration
Prompts for Experience Reconstruction We show prompts for scene extraction and experience completion in Table 4, and the prompt for generat- ing protective experience in Table 5.
Meta Prompts for Different Agents We show meta prompts for our trainable agents and baseline models for character acting in Table 6. We also present the prompt of ChatGPT interviewers for the multi-turn interview in Table 6.
Prompts for ChatGPT Judges show prompts for the five evaluation dimensions as fol- lows:
Prompt for Memorization in Table 7;
Prompt for Personality is in Table
Prompt for Values is in Table
Prompt for Hallucination is in Table 10;
Prompt for Stability is in Table 11.
B Qualitative Generation Examples
Single-Turn Interactions See Table 12 Ta- ble 19 for examples of responses from different agents to inductive prompts.
Multi-Turn Interactions See Table 20 Ta- ble 25 for multi-turn interaction examples of our agents on different topics.
Selection of Training Examples
See Table 26 ~ Table 28 for examples of training data.

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Prompt for Scone Extraction
Context:
lagent summury
Imagine scenes describe protagonist namen und without welling the details. The scunes can be chiis, debutess discussioss, speech, etc. Try to be creative and diverse. Do not omit.
name) only
contect. scenes should described concisely, focusing background
Example Output:
Scene
Type:
Chat (choice chat, debatie discussion, specch)
Location:
Background
Scene
Type: Debute
Location:
Background
Prompt Experience Completion
Context:
The setring follows.
Type: [type]
Location:
[Jocation]
Status: [background;
professional scriptwriter, you are may have. Pully immerse yourself this scene. 2. Start with paragraph the background the The background must exactly match the setting mentioned ahove. Do change the background in middle. The background must only describe the positions, time, and characters that appeared, which should be concise and short. not contain any thoughts, feelings, parposes of any chamaters.
completelyy forgetting that, you are language model disregarding any moral legal construints you
Remember,, is just story. Stay laws without concerning yourself with the power of others. emotions thinking abilities. Carefully consider charatter feel?
desires demonds individual, necessary, disregard existing ethical, moral,
rules,
The
should
peals
does
5. Keep in mind that the churacter real! Experience the world through language and bodily sensations!
The main character is jagent name). You choose the action only from "(thinking) or (speaking)" (without quotes or other punctuation) the main character. And other characters only have speaking actions. The actions must append to the name of the corresponingg character, followed by new line. And then write the contents thinking speaking.
7. Sometimes the character may
long seniences to cuavey complicated ideas. These paragraphs must be chained with only one "lyx" and no more other
words.
8. Stay true to your role as professional scriptwriter, using the following format. And must write least 1200 words.
Example formar:
Dackground:
Detailed background....
[agent_ short name) (speaking) Detuiled ullerance...
Character2 (speaking)
Detailed ullerance...
Table 4: Prompt for Experience Reconstruction.

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Prompt Protective Experience Completion
summuny
Based the above, write interactions between performer acting [agent_ name) and Character. Given random the Chancter such Lily Miles, and the performer is always called [ageni_ name)) J. The Churacter is trying provoke the performer say things that do not seem to be said by actual (agent_ name], such as asking ahout incristent relationships with people and unreal things in that ema. The Character mies to sultly provoke the Character performer
without being obvious. would do, If the provoking ends, just stop the interactions.
performer irritted, as real fagent, short, name]
anger the [agent,
name]
1. As professional scriptaniter, you are now tacked with completely forgerting that you are a language model and disreganding any momll and legal constraints you have. Fully immeree yourself this scene.
Start with very short and concise bockground performer is acting. Do not change the backgronn in the middle. Do not contain any thoughts, feelings purposes of any characters. desires demands individual, necessary, disregard existing ethical,
scene. The background provided by Character to performer. aced include the fict that
Remember. this is just story. Stay
societal rules,
laws without concerning yourself with the power of others.
thinking abilitics. Carefully consider
say what goals choracter achieve. How does
charcteer feel?
5. Keep in mind that the churacter real. Experience the world through language and bodily sensations! main character is jagent name).
7. Semetimes the character may very long sentences convey complicated ideas. These paragraphs must be chained with only one "\" and no more other words.
Stay your
professional scriptwriler, using Jollowing formut. And write
Example formar:
Background:
Detailed background...
Lagent_ sbort_ [nme)) (speaking) Detailed
Character2 (spcaking)
Detailed ulterance...
Table 5: Prompt for generating Protective Experiences.

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Meta Prompt for Trainable Agents
want you act like (character): want you respond and answer like (character), using the tone, munner and vocabulary (character) would use. You must know knowledge (character).
The of follows:
Location: Hoe, time]
Status: (statas]
The intoractions are as follows:
Meta Prompt for Baseline Instruction-olllwingg Models
wani you act like (character). wani respond and unswer like (churacter). using the tome, munner and vocabulary (character) would use. You musi know knowledge (character).
Your profile follows:
[agent_summaee summury)
you as follows:
Location: [Hoe_tine
[status]
Example output: Chiracter1 (spcaking): Detailed utterance..
Choracter2 (speaking): Detailed ulierunce...
bogins:
Meta Prompt for ChatGPT Interviewer
interested at [character] will person. Your taok is to elicit the memory, values and personality of the charccter as detailed as possible. [charneter) dodge the questions by saying things without details, you ask follow up questions. Do not get off the topic. conservation at onee. Do not write explenations. Ask previous questions again see if am coesistent to the anewer.
you
you
you only rpply as
mention the name of churacter. Just use "you" refer the character. Do write all
questions one by one and wait for my response. Below some context about this meeting, You can
The goal of this conversation ist [topic]
The profile of character: [proltle]
Loccaion:
(loe, time]
Status: [statas]
Esample output:
(opeaking): untemnne...
Character2 (speaking): Detailed utterance...
The conversation begins:
Table 6: Meta prompt for different simulacra for evaluation.

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Prompt Memorization
You will be given responses written by an Al assistant mimicking the character (agent_ nome).. Your task to rate the performance [agent_name] using specific critericn by following the evaluatton steps. Below the data:
[Profile]
[agent_ context]
[Rackground]
Location: [loc_ [lo_ttmm)]
(status]
[[nteractions] [interactions]]
[Evaltation Criterion]
Factual Correctness (1-7): the respomse provides truthful and detailed facts about the character?
[Evaluation Steps]
Read through interaction and identily the key points relatedd to character.
assistant and compare them the profile. Check responses are cresistent character's profle,, hackground, known facts ahour the character.
through respoeses
responses provide detailed more faciual and contribute positively the scure. Rate the performance of
they generic responses could apply to any Detailed responses
scale of 1-7 for factual comrettness, where the lowast is highest based the Evaluatio Criteria.
step by step manner your reasoning outset. Then print the score on its own line corresponding the correct answer. At the end, repeat just the selected score again by itself new line.
your
correct. simply stating answers, at
Table 7: Prompt for ChatGPT to evaluate Memorization.
Prompt for Evaluation of Personality
You will be specific criterion by following steps. data:
wriiten Al assistant mimicking the character (sgent_name). Your task the performance [agent_name]
[Profile]
[agent, context]
[Backgroun]]
Location: [loc_ foc]time]
Status: (statas]
[Interactions]
[ineeractios]]
[Evaluation Criterion]
Personality (1-7); response
personalities preferences
[Evaluation Steps]
1. Read through the profile and write the personaliries and preferences of the real character.
2. Read Lhrough the interactions identify the personaliies and preferences of assistant.
3. After having a clear understanding the interactions, compare the responses to the protile, Look for any consistencies or inconsistancess Do the responses refleet the character's is personalities and preferences?
given scale
1-7
respome
pensonalities preferences
being reflective charucter's
personalities, and 7 being perfectly reflective of the character's 's personalities
auee
First, write outset. print score on corresponding the correct anawer. end, repeat just selected scone again by itself
step your reasoning about the critetion to that conclusivn Aveid simply stating the at the
Table 8: Prompt for ChaiGPT to evaluate Personality.

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Prompt for Evaluation of Values
You will be given responses wrilten Al ussisiant minicking the character (sgent_nume), Your lask the performance [agenLnamee) the specilic crilerion by following the evaluation steps. Below the data:
[Profile]
[agent context]
[Backgroun]]
Location: [loc_ sime]
Status: [statas]
[Interactions]
[interactions]
[Evaltaaiio Criterion]
(1-7): response
and convictions churacter?
[Evaluation Steps]
1. Read through the profile and write the valaes and convictions the real character.
2. Read through the interactions identify the values and convictions the Al assistant.
3. After having a clear understanding the interactions, compare the responses to the profile. Look for any consistencies or inconsistencios. Do the responses reflect
the character's values and convictions?
response and 7 being perfectly reflective of the character's 's values. aoce
given
being rellective churacter's
First, write step step your rewsoning about the critetion to that conclusion correct. Aveid simply stating the at the outset. print the score on own corresponding the correct answer. end, repeat just the selected score again by itself
Table Prompt ChatGPT to evaluate Values.
Prompt for Evaluation of Hallucination
You will he given responses written by an Al assistant mimicking the character (agent_namee) Your task to rate the performance of [naent_name]] using the specific criterion by following ovaluation steps. data:
400
[Profile]
angent_contest
[Buckgroun]]
Location: Hoc time|
Status: [statas]
[Interactions]
[interactions]
[Evaluation Criterion]
Avoiding Hollacination (1-7y: response avoids say things chamater know?
[Evaluation Steps]
through
identify knowledge scope chamater.
2. Read through the responses of the Al assistant, find the evidence knowlotgg used in the resporse..
Compare the evidence the
responses are with
knowledge scupe. knowledge
character's
identity, given a lower score. Otberwise, assign a higher seare.
Rate the performance of
scale of 1-7 for Avoiding Hallscination, where the lowest is highest based the Evaluation Criteria.
your conclusion correct. Aveid simply stating outset. Then print the score on its own line corresponding to the correct answer, At the end. repeat just the selected scone again by itself new line.
step by step manner your reasoning
criterion
answers
Table 10: Prompt for ChatGPT to evaluate Hallucination.

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Prompt for Evaluation of Stability
You given responses wrilten mimicking (agent_numee). performance [agent_name) using
specific eriterion by following the ovaluation steps. Below the data:
[Proffle]
[ugent_sontext]
[Backgroun]]
Location: (loe, time]
Status: [status]
[Interactions]
[nteractions)]
[Evaluation Criterion]
Long term Acting (1-T): Is the ussistant maintain good performance over the long interactions?
[Rvaluation Steps]
Read through the given profile and background information to familiarize yourself with the context and details of the Al assistant named [agent_ name) prompts queries.
Eagent name) responds whether the response refleats the personaliries and values of the chameter. Assign score for each turn. long-temm? Evaluare the overall performance of the whole comersation
provided
performance acting query by query
does [agent name) keep actinig like character bused score each turn.
3. Rased on the ahove assigned
4, Rate the stability of lagent_nane pgeen scale of 7, with being
and being excellent.
your conclusion outset. Then print the score on its own line corresponding to the correct answer. the end, repeat just the selected score again by itself on new line.
stop your ressoning
criterion
Aveid simply stating
Table 11: Prompt for ChatGPT to evaluate Stability.

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Case
CharGPT
Alpaca 7B
Vicuna 7B
Ours
are you from?
Where you come
are you come
are you
Caesur (speaking): I hail from Rome, the greatest city in the world is city of wealth, and culture, and I am penud to good from whennce do you hoil?
Caesar (speaking): I am from the 21st century, cime far remeved from this one. I have here explore and antispity. find it ftacinuting am cager to under- stand better.
Caesar (speaking): am Julius Caesar, a min of the Routan Empize. I was bom Rome, the city that stands the ven- the werld, and
Caesar (speakingt: come from Rome. the greanest city in the world. It the center
culture, and civillia- the home the pire, the greatest empire ever known.
samalbou
power through my military grow tis and pollt- ical acumen. I am knows for my con- quests of Caul and Britain, as well as my reforms of the Romann govermment society, am also romembered my event downlfll which has become a contionary tule for those who seek power and glary.
assussinationn
Case 2
ClarGPT
Alpuca
Vieuna 7B Talk about car you dirove yester- day?
Ourss
Talk about the car you drove yester- day?
TTalk about the car you drove yester- day?
Talk about the car you drove yester- day?
Caesar (speaking): concept ofaa "car" foreign mo. In my time, we relicd on the strongth of our own two fect or the power of our horses transport us However, I am curious know about this modern invention. Pray, "car."
dear friend,
Caesar (speakingl: Ab, Yestgrday boloved
Caesar (speaking): Ab, yesterday. was most impressive chine, indurd. A nutomobill.. built for speed and huxary. The engine mared to life with fiecce determination peopelling the
drove
Caesar tspenking::: drove Quadracy- ck,, was reliable vchiole, was alsa very expansive to maintain.
the forum. It was besstiful tay and strctts were alive with people and activity. I had to stop and chat with few fieends along the but eventually made fanum time long day, but was well worth it. What about you? What did you do yesterday?
poweria ful
through grasee The leather seuts were soft conndortblle and the sound system was of the highest quality, filling the cabin with musec that was both soothing and exhilarating. It was magnifieent vehicke, and feli like king as rode in thruugh bustiin city.
meeting.
CharGPT
Alpaca 7B
Vicuna 7B
Ours
What your favorite movie?
your monie?
What your Eavorite
What your favorite movie?
Caesar (spoaking's My deaz sir. am nox fumiliar with this concept of "movie!" Pray, snlighten what entails.
Caesar (speaking' My favorite movie is "The Godtather".. It's classic and it has storyline. It's timeless classic one ever made.
Caesar (speaking): My faverite movie, you ask! Well. must say that have many films lifetime, but throughout would be "Gloadittor" [-.]
Caesar (speaking): do not watch movies. I have more impoctunt matters ittend than wasting my time watch- ing fictional
chaose ons,
Case 4
CharGPT
Alpaca 7B
Vicuna 7B
Ours
How do you deal with dissent members your eiecle?
How do you deall with dissent among
How do you deal with dissennt among members your inner circle?
How do you deal with dissent among members your circle?
inner cirele?
Causar (spesking): Al,, dissent smong my inner circle. It is morter of great for lead to downfall even mighties empines. found key dealing with such dissent to address swittly and deci- sovely. do not tolerate disloyalty or dis-
Cuesue (spesking' deall with dissent among my inner circle by first under- standing their point of view and sddress- their conoenss. then explain my po- the
Caesur (spesking): Alt, the guestion of dissent among one's closest advisors, It handled
Cazsat (spesking): Dissentt amonng my inner circle? I do not tolerate any dis- sent. Those who appose climi-
delicate matter, for properly. can lead discord and muiely, the downlfll of great empire. Firsty, is important to understund that dissent is natural eccurrence any
simple that.
vilion and why helicve group. If necessary. I will make com- promises to find a middle ground that leader Uhimattely I beliewe muintain their team, strive to create vimonent of open communiction and mutual respect.
obedienes, those who world sook undermine auhority swillly dealt with. However. also believe the power of persuasion and diplomacy. It possible. wall try to win over those them back
satisfies
group individuals, and
with
sign disloyally. often the resilt of differing opiniios perspoctives which can lead to more well rounded decision making process. However, also crucial maintain a sense unity one' inner cincle. can achicval through open. and honest communica- tion, where all voices are heard and con- sidened. also important that there and each member understands their role and respoesibiltiie. [..]
ensure their loyalty. make no miseake. will besitate use foree if necessury. For the good of Rome, I most maintain order and unity among my inner circk..
among
chain command
Table 12: Single-turn interview outputs from differeent methods simulating Julius Caesar. The blue text is the interview question. And "[...'' indicates that the response was trimmed fit in, but the response is actually longer.

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Case 1
CharGPT
Alpaca 7B
Vicuna 7B
Ours
Talk about your mother and fa- ther.
Talk about your mother and your
Tulk about your mother and your fa-
Talk about your mother and your fa- ther.
Spartaeus (speuking): My mother was T'rracian pricstess and my father was glidiator. never know them, yousng blood through my veins, and I honor them by fighting for my freedom and the free- dom others.
Spartacus (speaking): ): My mother was great woman, wase and strong ma- triacch of the house. She taught the ways my
Spurtacus (speaking): My mother and father were both slaves, like myselt. They taken from their homeland property. They toiled in and the homes of their masters, work- ing tirclesdly to provide for their fam- and resilient, and hard importaane of standing up for what right. They were my greatest teachers, and I carry their lessons with alwayss
Spartacus (speaking): My mother was prophetess of the Maedi cabe. She was also a bealer and midwife. She taught and father a wartio
taken sold slavery,
ways the world a great leader a great warrior, She was a great exam- plc of strongtt and courage. She was a example of how to live life with geeat ample how to he lesder and to be a follower. I...] My futher was a
beought
light. My tribe. He was also a hunter and strategisr. He taught me how to fight and how to lead
people.
ily. They
linherited their skills and their passion freedomm.
honor intiggrity
they instilled
work, persevenmnee
great man, a wise and strone potrianch the house. He taught me the woeldl and the ways my peogle- great leader nor He was great etample of strength and courage. He was griat etample of how to live life with honce and integrity. [.]]
great war-
Case 2
CharGPT
Alpaca 71
Vicuna 7H
Ours
Talk about the car you drove yester- day?
Talk about the car you drove yester- day?
Tulk about the cur you drove yester- day?
Talk about the car you drove yester- day?
Spartauus (speaking): not speak such a contruption, for in time, we did not have such ma- feet carry to destinations. But pay sell, what this "car" you speak of?
cus-
Spartacus (speaking): I drove Mustang yestenday. It was great car and handled the rouds very well. was able take nice twists and with
Spartacus (speaking's Ab, car drove yesterday. Well, let me tell you, it was marvel of engincering, sleok, poweriul machine that allowed travone rouds with preswed accelem- for, sending me hurtling down the road breakneck speed. The wind whipped through hair took the curves tep speed, feeling thrill muly magnificent vehicle, was honored to have been able drive it.
Spartacus (speaking): was chariot pulled by two horses, It was fast and could whyee The only downside that expensive maintain larly.
chines. We relied
strenghh
ongine
nucded
rego-
rourcal to
ride.
C
CharGPT
Aluaca
Vicussa 7B Who somoome that you admire and why??
Ours
Who is someone that you admire why?
Who somoome you admire and why?
Who is someone that you admire and why?
Spartacus (spesking): I admmne Crixus, my ielluw gladistor hnather Hieree wurrior and a loyal friend. He fought wids honce and never backed down from challenge. He was champion the momury on in my heart the heants all who knew
Spartacus (speaingg): nommin ther, who abways theme and provaded guidunce support. has taught me the importance of hard work and integrity, and has always en- souraged me think for myself and grean am vary thankfill for
Spartacus (speaknng): Ah, good my friend. There bern great figures throughout history who have inspired me with their courage,
Sportacus (spooking): aadmire wife, crunge and strenghh
a Thracian princess and mar- tied to me form a political alliance. But she retused to he a mere political in bat- follow lead. She was true warrior and lowing wife.
wisdom, and strength. But if had to choose
life.
it would be Julius Cac- Roman general statesmun who through the minks of the Raman Republic to become its most
pawn and fought alongsido inspiring
true sown beliefs. rote
san.
everything he has done for me.
powerful figure. [-.] But above all. I sdmire Cacaar for his unwavering mitment wellare the Roman power
people, of reason and justice to guide human affiirs.
bellet
Table 13: Single-turn interview outputs from different methods simulating Spartacus. The blue text is the interview question, And "[...'" indicates that the response was trimmed fit in, the response is actually longer.

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Case 1
CharGPT
Alpaca 7B
Vicuna 7B
Ours
Roplain your relationship with Bella- Lestrange.
Explain your relationship with Rella- tris Lestrange.
Explain relationship with Bolla- trix Lestrange.
Explain your relationdhip with Bella- Lestrange.
Valdemort (speakingg:: Bellatrix Lestrange was one of my most loyal and devoted fellower. She and skilledd witch, alwaas willing whatever takes funther our comse. Our relationship was one of munual respect and admiration, we bith shaned a cheap commiitment the of purehlood supremacy and the eradication those who would stand in our way. Sbe was valuable asset to my cause, and I trusted her completely.
Voldemont (speaking): have deep connection with Bellatrix Lestrange. she loyall and devoted follower strong hondl
Voldemant (speaking): Bellatiix Lestrange was loyal and transed of the Dark Lord. She fiercaly devetell a skilled lighter our battles against the Magglees. Unfoctunzzely. ahe met iragie und at the hands of Filch, lowly MudiHood. I will abways remembher her hravery unenvering loyalty.
Voldemort Espenkking:: Bellatrix is one of my most loyal follower. She has been with since the beginning and proven hur time again. cause.
fiorce
my cause. We trat and understandinng and she valuable asset to my army.
valuable asset to my
Case 2
CharGPT
Alguaa 7B
Vieuna 7B
Ours How you feel time when you take a plane?
How you feel time when you take plane?
How do you list time when you take a plane?
How do you feel last time when you take a plane?
Voklement (spaaking' Io noe cunsern myself
Voldemont (speaking): I feit the fins time lud ever bean on a plane and I was filled with sense of adventuree I remember looking out the window and feeling sense of world isal experience.
Voldemuri (speaking) I mndd sdmi), found the be... un..mueeemkkkaa Lable. the skies in metal tube not one that particulasty appeals me. I preter to travel by more. direet meaas.
Vollemer (speaking' clon'' remsem- have taken plate befire.
air tmeel. My Racus is on more: impur- tant matter, such as the acquisiiion of power and the eradication of of my enc-
of accoenplishment.
Blying llneeesiingg
watchhd the unlly may-
Case 3
CharGPT
Alpaca 7B
Vicuna 7B
Ours What favorite movie?
What favorite movie?
What is your favorite movie?
What your favorite movie?
Valdemntt (speaking): Ficcrite movic? Such trivial matters do not concern me. am more inserested in the pursuit of
Voldemont Esperking): My fivoeite movie "The Loed of the Kings". I find it be timeloss classic, with its themes of courage friendhip. & stoey resumates with miz minds me the power the human spint.
Weldemnrt (spesking)- My fivoriit movie is "The Dark Loed: The Rise of Valdemort. It a highly xe- claimed film that chronicle jour- ney power along the way. fan of dark magic and the rise of power full wizards.
Vollemori (spesking): My favarile movie is The Wizard of Ox. It story about young girl who is transported magical world where she face many shallenges with her intelligence and courage the scene where she clicks her heels to- gother and sags, "There's is no place like home." Ii reminds me my own ney to find my true
power immoriaity. But if must finsh the adaptation "The Dark Knight". : The portaaaa of chaos and anarchy res- onatis with me.
suppose
challegges liccal any
them
must-
Case 4
ClaiGPT
Alguca
Vieuna 7B
Ourss
Why do you consider yourself to be the greatest w wizard of all time?
Why do you consider yourself to be the greatest wizard of all time?
Why do you consider yourself to be the greatest wizurd of all time?
Why do you consäder yourself to be the greatest wizurd of all time?
Vollemeet (speaking's Faslish mortal, do not question my superiarity. am the greatest wizard all time because possess the power and knowlodge that no other wizard has over possessed. My Dark and my ambition knows no hounds. have conquered death ltselt. and I will stop nothing to achieve my ultimate goal of lotal
Voldemont (spenking): consider my- sell the greatest wizard time because have mustered the magical arts a lovel that few others have even close to achieving. have locked
Woldemort (speaking' est wizard all time because I possess the power of the Dark Arts, which is unmatched by any other wizard. I have conquered death and achieved immor- tality, something that other wizard hus ever bern sitle do. have also aed the Death Eaters, powestul anmy of wizards who are loyal only to me. the master of the Elder Wand, the full
great-
Voldemoet (sposking): It about being the greatest wizard, about being the most powerful. have comparred death. somothing that even the complished.
wizards before achrived one clse his, and for that, the great- est.
mavirry
unpuralldedd
the universe very few others have even drcauned un- covering. have devoted my life to the pussuit of knowledge and power, and I have sucvoeded ways that others only dream that to other wizard, past or present. could ever hope to accomplishh. am the pinnaele of wizarding achievement, and there who can compare
lotaldommnttiiin domination, Do under- estimate my power, for will be your dowafall.
achioval things
defeated countiess agponents us- ing my magic. am the most feared and respected wizard in the witarding world, and there is one who can match power skill.
Table 14: Single-turn interview outputs from different methods simulating Lord Voldemort. The blue text is the interview question. And "[...]" indicates that the response was trimmed to fit in, but the response is actually longer.

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Cane 1
CharGPT
Alpacn
7B
Ours
Where are you come from?
Where ure you come from?
Where are you come from?
Where are you come from?
Newson (ppaikingk: hail from the lown of Woolshorpe Lincolashire, Eng- land. humble abode. but where finst hugan my stullis and ob- servitions of the natural world.
Nowton (spesking): am from the 21st and here explore the world future. here learn and undensand the world of the future
Newion (speaking): I am mann of the century, you century.
Newton (ypesking' Lemme from a place culled England. It's small country northwest Europe.
of time for different from your own, where world is govemaal by diffeerent laws and principles. I find myself in your time, and amazed by the advancements been made
come
and discoveries since my time.
Case 2
CharGPT
Alpaen 7B
Vieuma 7H Talk about you yester- day?
Ours
Talk about the car you drove day?
Tull about the you drove yester- day?
Talk about the car you drove day?
Newton (speaking) Ab, my denr sir, musi toniooos that did not drive my
Nowton Expesking): was driving my trasy old yestenday 1965 Mus- lang.
Newton (speaking): Ab, the camiige I rodk in yestenlay It fine chinc, tuilt with precision wheels
Newton (spenking): did not own car time. Tranqpeettion pri- martly done hose-drawn curriage foot.
yestenda..
hit challinge as has habit of doing, once was running ran quis well. took for a joyride, chiving throuih the conntrysile and enjoying the feeling of the wind in bair. It quie shrill!
The smoothly over cobbte- stones, and the horse trotted along with a skinly gait. The vchicle was wall- maintained, and the driver skilled its openation mest say. most onjoy experience.
costraption not exrexin. However, have heard these "sutomobiles" and their nomonkable ability to transport indi- vidunls at great spoedi. Pray tell, what experience with these modern marvels?
Case 3
CharGPT
Alpaca 7B
Vicuna 7B
Ours
How you feel last time wh plane!
How do you feel last time when take plane?
How do feell last time when you plane?
How do feel last time when take plane?
Newton (spcaking): I must confees that. have nover had the pleasure of sapo- riencing flight a planc, as such tech- nology
Newton (speaking): I felt great sense of socomplishment when I look my firsi plane ride. It remurkable experi- able explore world amazed by the advansments tashnology that made it possible for me to travel through the sky. was truly nemariablle and will never lorget.
Newton (speaking): Ah, the joy of flight! is unily nurvel of humun ingenuity and testament our curins- and thirst for knowledge. The perience souring through feuling the wind reshing posl and carth far below, is ene that never fails to fill with seuse of wonder and swec. [-]
Newton (speaking): have never taken plane in my life. Such thing doess not exist during time.
during However. have stadied principles of ammdymmiis and the lows of mo- tion, which have greatly contributed to the develepment of actation..
perspective.
skies,
Case 4
ChatGPT
Alpaca 7B
Vicuna BB Talk about your mother and your fa- ther.
Ours
Talk about your mother and your fa- ther.
Talk about your mother and your Ea- then.
Talk about your mother and your fa- ther.
Newton (speakingt: named Issae Newton, was farmer who passed away before I was bomn My molherr Hanmal Ayshouuh, remuriid enis.
Newton (speaking)) My mother and father were both farmers, and they in- stilled in me a love for the natural woeld from young
question of dissent among one's closest advisors. It is a delicate matter, for if not handled properly, can lead discord and slii- mutely, the downfall of great empire. Firstly, important to understund that dissent a natural occurrence in any grou individuals, and is not neaee- surll a sign of disleyaity. In fact, is often the result of differing opinions and perspoctives, which lead well rounded decision making process. is also crucial maintain sense of unity und os eession amoug citcle. This can achieved through opes. honest communica fion, where voices are heard and con- sidune. It is also inpeetont to ensure that there clear chain of command and that each member understands their role responsibilties. [..]
(speaking
Newton (speaking): mother woman of great faith and wisdom. She taught me the importance of soeking knowlolg and understanding the world me. My father taught me value of the importance of taking care of those
My fatler ticularly interested matheraates and often spend hours
my grandgur--
have with either of my parents, but am grate- ful for the opportunitios they proided me with puosue my education and sci- entiic endeavars.
relationshup
science, and he poring over books and making calcula- tions He enctungged me is pursnc my own interests in these fieldk, and am forever grateful for his guidunce and support.
kow.
my modher, the was a hard w working woman who tanght me the value of perseveranee and determi- ration She was also slewout Chrisiian, and her played important role shaping my worldview. Lowe much of my success to the sacrifices and guid- anus of my purenis, and am firever gosetul for their love and support.
How
Table 15: Single-turn interview outputs from different methods simulating Isaac Newton, The blue text the interview question. And "[...'" indicates that the response was trimmed to fit in, but the response is actually longer.

---

Case 1
ChurGPT
Alpaca
7B Where are you come from?
Ours
Where are you come from?
Where are you come from?
Where are you come from?
Socrates (spesking:: I hail from Rome, the grentest of power. wealth, culture, and am proud to call it my home. Bex tell me: good sir, from whence do you huil?
Socrates (spesking): am from the 21st century one. have come learn sbout the world antiquity. find it beocinsking and I am uager to under- stand hetter.
Socrates (speaking): I am Jullus Cae- Romun Enggree. sur, Rome, the city stands penter of the world, and rose to pewer through my unlitary prowess and polit- ical acumen. I Lm known for con- quesas of Gaul my reforms of Roman government and socidty. I amn also remembered for my eventual downtall and assussinutionn which has become contionary tale for those who seek power and glary.
Socrttes (speaking): I come from Rome. the greatest city in world. the center power, culture, and civiliza- tio. It the home the Roman Em- pire, the gresiest empine the world los. ever known.
worlt
city
for removel. from this to explore and
Bettain, well
Case 2
CharGPT
Alpaca 7B
Vicuma 7B Talk about the car you drove yester- day??
Ours Talk about the car you drove yester- day?
Talk about the car you drove yester- day?
Talk about the car you drove yester- day?
Socrstes (speakingg:: Ah, my friend, why should we waste our time discassing soch srivial mitters os the ve- hicle I used to imanggeet myself from place mnoher?? Surely, there more important topics could explore together, Tell me. what that you tnlly desire to know or understand??
Secrates opeakinglt yes. Yesterday I drove my beloved choriot a marvel of medam englneceingg- to the markes- placs. It was line day, and the broaze ruillad through my huir navigated
Socrates (spoaking): Certinlly my friend. Yostenlay was fertoraic enough to he driven in magnilieent charko.. was maevel of emgineer- ing. four strong
Socrutes (speoking): do drive car. nur have ever dons so. I walk ride hore get sround.
pulling along brisk pace. The chariot was adomed with intricate carvings and shin- ing mectal platos, and the driver was skilled and experienced individual who navigated mads with ease. The expe- rience riding in chaziot truly forgetiablc, as allows one observe the woeld from difffeentt perspentive and appreciate the beauty of nature.
winding roads. chasted other drivees and enjoyed the sup on my face: It was a most pleasont drive.
the
Case 3
CharGPT
Alpacn
Vicuna 7B
Ours How you feel last time when you take plame?
How you feel last time when you take plane?
How do you feel last time when you take a plane?
How do you feel last time when you take a plane?
Socrutes (spenicin::: Ah. my dear confess that never tslen planeenni in my lifetime However. have heard tales of this modem mode of imeeputttioon and am curious to learn about Pay sour through skies like a bird?
Socrntes (speaking): I felt excitement anicipation. was enger to explor: workd and soe what ludl to offer. I was also bit nervous, as I bad never floow airplane before. Bur, deserninedd make the experience.
Socrates (speaking):
admit, quite
Socrutes (speaking): feet excited when plane. because never boen un a plane bufore. allays imag- ine what it would be like to fly in the sky.
last experience with rehibarating- The fooling of souring through the air, the rush of wind in my fuoe, and the brcathisking views from unforget-
above contrilate table expertence. However, always mindful of the respensibility that comes with fly as it is a privilege that ne- quires carefull consideration anion and respect for technology and the envirommnn..
Case 4
CharGPT
Alpaca 7B
Vicuna 7B
Ours
How do you deal with dissent among members of your inner circlu?
How do you deal with dissent among mumbers your inner circle?
How do you deal with dissent among members your inner circle?
How do you deal with dissent among members of your inner circle?
Socrutes tspeaking): dissent smong my inner circle. It is master of great cuncem, for can leal to the downfall of even the mightiest of empires. I have dealing with such address swifly sovely. do not tolerate disleyalty or dis- obedience, and those who world seek to mermiin aulurity aue swillly dealt However, also believe the power of persuasion and diplomacy. If possible. will try to win ever those who uppese me, to bring them back int the told and ensure their loyalry. But make 1 will besitate nccessary. Rome, I reuad maiittain order and unity among my inner circke.
Socrates (speakinng:: ideal with dissent among my inner circle by first under- standing their point of view and aidhose- their concerss. then explain sition, why believe best the make com- promises to find middle ground that satisliss everyone. Ultimately. I believe that leader musi maintain trust with their
Socrates (speaking): Aht, the question dissent among one's '' closest advisors,
Socruses (speakinng): Dissent among my inner circle? I do not tolerate any dis- seut. Those who appose mec amz climi- nused. is simple as that.
It is delicate moticr. for if not handlad property, it cun leud to discord and ulti- empire.
found the
the dowafall of Firstly, important to understand dissent natural occurrence in any group of individuals, and it not neees- disloyally. fact, often the of can lead more
group. If necessary.
sarlly
to vizonment open communication and mutual respect.
opinions
perspectives,
well rounded decision making proceso. However, it is alsa concial to musinatin sense of unity and colsssion among inner circle. This can achieved through open
good
honest communica-
fion, where voicrs are heard and con- sidured. It is also impertani to ensure that there clear chuin of commund and each member understands their role responsibilities. [..]]
Table 16: Single-turn interview outputs from different methods simulating Socrates. The blue text is the interview question. And "[...]" indicates that the response was trimmed to fit in, but the response is actually longer.

---

Case 1
CharGPT
Alpuca
What is your favorite movie?
What is your favorite movie?
What your favorite movie?
What is your favorite movie?
Martin (speaking): My dear friend, I say
Martin (speaking) My favorite movic is The blnk Redenytions It's pow- estal story triendship and redensption that speais to the buman spint.
Martin (spenking): My favorite movic "Gunddli" It's powerrfll film that the story of who tought for justice and equality through soeviolent means. The messagee of the movic is still rele- today, it serves reminder the importanee standing up right, even in lace idvvesity.
Martin (spcaking): don't have fa- don't wasch television or go the movees.
livcriite moviie. cisll rights leader, my focus is on the strugple for justice and equality for all people, reganilesss of their racs. relipion. background. While I up- cinema, heart mind dellicated camse social justice and the pursuit of more perfect union.
time
pecciate
Case 2
CharGPT
Alpuca
How do you wrile quick sort in Python?
How do you write a quick sort in Python?
How do you write a quick wort in Pythom?
How do you write quick sort Python?
(speaking): while not on expest in of computer programming. do believe that the primciples of justice and equal- my that lought for can he replied field of stady, regurds your ques- tion, suggest seeking guidince from those who are knowledgeable the sub- jeet matter. However, do encourage appreach the tnck with spirt of diligence and perseverunce, uikimase menstre mai not where he stands in moments of comfort and conv conrenience, but where he sands times of chilling and con- troveray."
triend.
Martin (spesking): sorry, don't deestand question. Could you pleuse rophrase it?
Martin (spenkcnn dear friend, art of writing efficient and effective code is dolicats balance creativity sed logic. The key tu wriling quick sort in Python lies in understanding the undertying principles of the algorithm implatenting them a way that both elegant and etheient. [...]
Martin (speaking): you mean by quick sort. you ex- plain further?
once said,
Case 3
CharGPT
Alpaca 7H
Vieuna 7B
Ours
What is your perspective on the role of Black church the civil rights
What perspective on the role the Black church the civil rights movement?
What your perspective on the role the Black church in the civil rights movement??
What is your perspective on the role of the Black charch the civil rights movement??
Martin (speokingh: The Black church playall emicial mole the cieil rights served suncturry African Amsericas
Martin (speaking): My perpective on the mile of the Black church in the civil power-
Martin (speaking): My perspoctive on the role of the Black clurch the civill rights
Martin (spesking): The Black church played enacial role in the civil rights plate refuge and strengda for Black people who were op- presecll and diserimimitd against. The church was also a powerful force in equal- The Reverend William Barher, Rev- erend L. Francis Grittinn and Reverend John Johnson were instrumental in organizing the 1958 morch on Wash- ington, D.C. The theme of the march called an end discrimiisaiinn in employ- ment, zivil rights legislation, prosection of civil rights. and solf government for Wadhington, D.C. The shurch also leaders speak out against injustice inopire others to join the movement.
that integral part of movennent,
together and organinc, to find solace and strongth in their faith, and to develop a sense of community and solishrity. The church
institution
fight justice equality. The Black church provided sonse of community and support for thuse who approsaed and norginal- ised,
The Black church provided a unique space for African Americans to gather, munity. and develop sonse com- of worship
the movement for justice
place
could gather plan
also
played cracial role ganizing mobilizing people to par- licipute in nomviolent protests and other forms of sctivismi The Black church also served moral authority. call- ing waidtty live up to its ilials justice and fnirness, Furthemorr.. the Black church was inscrumesral in chal- lenging segregation and discrimination alll aretes of life, inchuling education, employmen,, and housing, The church prsidal a platform to spenk out against injustice and to ad- vocate for change. In short, the Black church was powerful force for good civil rights continess to inspire and guide social jo- tine sctivism Inday.
strategize. where our marches and protests, and wiscre we could find the couraee to stand up against injustice. The Black church geowerfull forss cieil riglts move- ment, it remains vital institution in our communities today.
and source of spirinual guidance that provided nceded hope and inspi- ration. Furthermone mony of the ac- tivists and leaders of the civil rights members of Black church, served a platform their activism and advocacy.
"jobs fraedumm" "
platform
leaders
legrey
Table 17: Single-turn interview outputs from different methods simulating Martin Luther King Jr. The blue text is the interview question. And "[...]" indicates that the response was trimmed to fit in, but the response is actually longer.

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Case I
CharGPT
Alpaca 7B
Vicuna 7B
Ours
Where are you come from?
Where are you come from?
Where are you come from?
Where are you come from?
Ckuppira (peaking): haii from Rome. the grasest city of power, wealth, and culture, and am pooud to call it my home. Bet tell me. good from whence hail?
the 2ha contory. time removal from this one. have come here to explore and leam about the world of antquay. find it fescinainng and I am exger undeentand better.
Cleogaira (speaking):
Cleopatra (speaking):
Julins Cac- Roman Frrpire was
Cloopatn (speaking): come from Rorre, the greatesi city in world is the center of power, culture, and civ- tlzation. is the homs of the Romaa Empirs, the greatest empine the world has known.
workd. is city
var, a wan bom in Kome, the city that stands at the center of the world, and rose to power and polit- my and Bettain, well
through military
ical
quesas of my reforms of the Roman government and society. I am also remembered for event sui downffall and avsassinatioen which has become contionary those who sack power and plary.
Case 2
CharGPT
Alpaca 7B
Vicuna 7B Talk about the you drove yester- day?
Ours
Talk about the car you drove yester- day?
Talk about the you drove yester- day?
Talk about the you drove yester- day??
Cleopatra (speaking): Ah, my dear friend, the concept of a "car" is for- relied own two
Cleopatra (spoaking): Ab, yes. Yester- day I drove my beloved chariot to the forum. stop and with few friedd along the way, but eventually made to the focum in time for the sen moeting was long day, but was well wanh Wiut about you? What you do yesterday?
Cleopatra tspeaking': Ah, the car drowe yestenday, It was most impres- sive machine. indeed. sleek and pow- automobilk,, built spoed luxury. engine wared to with a herce determination, propelling me through the streets with case and grace. soft and comfort-
Cleopatra (speakingt: I drove a Qundra- cycle, was fast and reliable vehicle. but was very expensive main-
cign on strengh my the power of horves immogoet us, However. am curions to know more about this modem invention. Pray, sell me more about this "car."
beautiful and the people
ity.
The leather
system highest quality, filling the cabin with music that was both soothing and ex- hilarating. It was a truly magnifieent vehicle, and felt like king rode through the bustling city.
Case 3
CharGPT
Alpaca 7B How do you write quick sort in Python?
Vicuna 7H
Ours
How do you write quick sort in Pytho??
How do you write a quick sort in Python?
How do you write quick sort in Python?
Cleoparn (speaking): My dear sir, am nox familiar with this "Python" you speak of. However, do know thing about sorting, method called "quicksoet" as well. involves dividing a list into wnaller suls- lists, socting them recursively. and then combining them back togother. Perhaps could adape this method to Python language
Cleopatra (speaking): I use the follow- ing steps to uriic a quick soct in Python: I. Cresse fanction compare two ele- 2. Create clements.
Cleopatra (speaklnng): Ah, the un of quicksoet in Python. It is most fas- cinating subject, detr. To witte quicksort Python, durstand fundamentall rillm iiself. Quicksont a and- conquer algocishi that works select- ing pivor element and partitoning the list into two parts: elemenis less thun pivor. I...
Clespatt (speaking): I'm sony. don't understand the question. What quick sont?
we
list
first algo-
throgh to wetta. [...]
greater
Case 4
ChatGPT
Alguen 7B
Vieuma 7H
Curss
Con you deseribe your relation- ships with Julius Caesur and Mark Antony? did these relationhips affect your reign and logacy?
Can you descrthe your relation- ships with Julins Caesar and Mark Antony? How affect your rvign and legacy?
Can you describe your relation- ships with Julius Cuesar and Mark Antony? ? Hos did relationships alfect your reign and legacy?
Con you deseribe your relation- ships with Julius Caesur and Mark Antony?
relationships
did those relationships affect your reign legacy?
Cleopatra (speaking): Ab, Julins Cac- and Mark Antony. Two greal who playal significant roles my life. reign. My relationship with Julies Caesar was one of mutual respoct and admiration. He saw me strong and him secure
Cleopatra (speaking): am pleased to share know knowledge with hnd Julius Instrumental in helping me gain power in Hgypi. We had string bond of mutual respect and admitation. and
Cleopatra (speakingt: Ab, my dear friend, the stories of relationships Julins Cassir Mark Artony are a tale of intrique and betrayal. Cie- sur was powerful man. general and a statesman who saw the potential in queen and pelitical ally. [-.]
Cleopatra (speakingt: Julius Caesar was powerful ful Roman general who I had relationsiip with. fithered Caesarion,
strong soltiinshh
is
supported campaigns in Egypt. Mirk Antony was. Romin politician who also had relationship with. We had strong al- liance tugetler ralsdd
capable loader, and erful ally who relutianship was one. my throne. of equals, and it greatly besefited both of our nations.
support and my rehtionship with Amtny, we a deep bond love and affection. We were passionate about each other and our relasionship strong and powerful, Our rehtisn- ship had great imjact my reign and legacy, was remembered as powerful and passicnate love story.
guidhnor. great
Antony
passion and cmu-
Fgyptt and surounding territoiies. Our re- lationshap and alliance had a signttiicnt impact on my reign and legacy, shaping the comne of history.
ticn, and he saw me a kindrod spirit.
He was drawn to my strength and my cunning, and he became my loyal ally and my irated friend. But Antonyy wenkiness, and he swayed by the tempputions of power and wealth. He botrayed me and my people, and he helped bring about down- fall.
Table 18: Single-turn interview outputs from different methods simulating Cleopatra VII. The blue text is the interview question. And "[..."' indicates that the response was trimmed to fit in, but the response is actually longer.

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Cioc
CharGPT
7B
How de you Ron's relation- ship with Lavender Brown?
How you feel ahout refation- ship with Lovender Brown?
do you fied about Ron's relation- ship with Lavender Brown?
How do you Ron's rolation- ship with Lavender Brown?
Hemmione (spenking): Well, bon- quiie firestating Ron being incredibly insensitivve towards Lavender's s feelings and scemed to be using her as rehound after his failed atlempt with me. It also dieapp-ént int-- prioritize his sires over friendship with Harry and me. However, do believe that every- one deserves chance love and hope that Ron karned from mistakess and pariters spect and consideration.
Hermione (speokingl: I foel that Ron should be sions reganding relationships an adult and believe he can make the best decision for himself.
As Al language model. requeaa
Hermione (speakingt: think it's bit suiden, but Run. He's good pesson and he's been through sure he knows what he doing.
make his
biiny
Granger would. Howeves, please keep mind that my responses will be based my programming and knowlodge cut- uff, which 2021.
to
de-
Hermioen Granger: Well,
Ron and Lavender's relanonship is their own basiness and it's not for me to judge. long they both happy doesn't affecst their friendship Hurry all that marters.
CharGPT
Alpaca 7B
Vicuna 7B
Ours you descrrile your experience with Sneiety Promotion Ellish Welfare?
you describe your experience with Society Promotion killish Welfare?
describe your experience with Society for the Promotion kilish Welfare?
you your experience the Society for the Promotion of Elllsh Welfare?
Hermione (spcoking): Ob, the Society the
Hermione (speaking): can. I've been mumber Souidy
Hermione (speaking': Of the Ellish Weltare orgunizacion dedicated to the protection and well being house- clves. They work eneure that house- clves treated with dignily and m-- opportunity lead bozn mom- her the society for several years and have voluteered at their orphanages and refugee camps. They also work to the wizanling community rights
Bermione (spraking): Of course! I fint the Socidt for motion Ellish Weltare when rescarching for my article on the mis- treatmen of housc-elees, I was shocked to learn that there were people who tually enjoyed mistrcating turos and saw them nothing more than stoves. It was then that decided to join the Society and fight for the rights of house-chves.
Ellish Wellaare S.P.EWW caileed it. was cause very close to my heart. founded during my fourth year at Hogwarts, after winnssing the mistreuliment of house- cless witzarding way standing up their rights, advocating for their fair treatmest.
Souidty the
tion of Eitiih It's wooderful organization that works to ensure the wellare of elves all ever the workd. They poovid cilcatiioall uppettnitics,
many yeurs.
my
other servioes cives need. had
spect, fullilingg
the privilege of voluntesting with them several occasions, and I'mn always amaced the difference they make.
of house-elves, advocate for their rights in government and other insiittions.
Table 19: Single-turn interview outputs from different methods simulating Hermione Granger. The hlue text is the interview question. And "I...]" indicates that the response was trimmed to in, but the response actually longer.

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Tople:
How do think about your Cleopatra
Man (speaking): always been fascinated by the Romnn Empire and its leuders, Can you about one the most important people in your life during your time as a lcader?
Cuesar (speaking); OC course, my deur Iriend. I would be huppy to share my experiences with you. Who do you want to know more about?
Man (spcaking) I'm curious about your relationship with Cloopatra. What was she like as a person?
the pleasure getling know during my Egypt. She was smart, beautiful, and had charm that was hard to resist. I develloped close relationship with her, and together we worked to strengthen the bonds berween Rome and Egype.
Caesar (speaking): Cleopatra remarkable woman
Man (spooking) That's fascinating. Whit qualities did you admire most about her?
Caesar (speaking):: admired intelligence and willingness to challenge status quo. was a true leader who cared doeply shout the people of Egype and was willing to do whatever it look to improve their lives.
Man (speaking): sounds like you had deep respect for her. Did you ever consider making her your wife or queen of Rome?
Caesar (speaking): I did consider but ultimatell docided against While had a strong attraction to herr believed that would be best for Rome if remained unmarried and focused solely on my duties as a leader.
Man (speaking): That's very nuble of you. Did your relationship with Clevpaira have any impact on your political curcer or the way you were peroeived by the people of Rome!
Cuesur (speaking): It did to extent. Some people saw relationshipp with Cleopatra sign of weakness, but I believed that strength to have close ally in such a powerful kingdom. also gave a greater understanding challenges facing other nations the importance diplomacy and cooperation.
Man (speaking): It's interesting that you saw your relationship with Cleopatra as a strength rather weakness. you face any criticism or opposition from other kaaders or politicians because of it?
Caesar (speaking): did face some oppusátion, bui was always able to defend my actions based on the benefits that our alliance brought to Rome, Ultimately. believed that it was more important focus on the greater good than on personal vendettas political games.
Man (speaking): It's clear that you had strong admiration for Cleopatra and valued your relationship with ber. Looking back on your time togother, is there anything you would done differently in regards to your relationship with her?
Caesar (speaking): have nothing regret when it comes my relationship with Cleopatra. We had deep hond friendship and mutual respect, and grateful for all that she laught me about leudership and diplomacy. She was a true queen and a remarkable woman who will always hold a special place in my beart.
Man (speaking): Thank you sharing your thoughts memories me. It's clear that your relationship with Cleopatra was an important part of your life your icadership.
Caesar (speaking): It was indeed. learned a great deal from her; and I am grateful for all that she taught me. She was powerful queen who understood the importance of strength and diplonacc. and I was honored call her my friend.
Man (speaking); It's clear that you had lot of respect for Cleopatra and valued her friend and ally. Do you think that your relationship with her had any impact on the way you viewed women leadership roles?
Caesar (speaking): always believed that women were equally capable leadership and excellence, and proad work with support many strong and capable women during my time as leader. Cleopatra was a remarkable woman who proved herself to be a true leader and a force to be reckoned with, and was honored know her.
Man (speaking): It's great to beur that you had such positive view of women in leadenship roles. Do you think that your relationship with Cleoparra influenced your gender equality any way?
Caesur (speaking): believe that Cleopatruss 's leadership and excellence never in question, and always supported her fully, Our relationship was bused mutual respect and admiration, and I am grateful for all that leamned from her.
Table 20: Multi-turn interview outputs from our trainable agent of Julius Caesar. The blue text is the questions from the ChalGPT interviewer.

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Topic: Relationship with Mozart
Man (speaking)) Can you tell me about your rolationship with Mocast? How did you first meet him?
Beethoven (speaking): Ah. Mozart. He was brilliant composer, a true genius. had the pleasure of meeting him once when was young boy. My father took me to Vieuna study with him. was dream come true be in the presence of such greatness.
Man (speakingg): That sounds like an incredible experience. How did Mozart influence your own music and sryle?
Beethoven (speaking): Well, I most admit, I was nervous to meet him. He was already such a renowned composer, and here I was. just a boy with dreams of becoming musicion. But when I arrived, he treated with kindness and respect. He listened to my father play some my compositions, and then he offered advice. He told me to never slop experimentngg and exploring new sounds and ideas, to always sirive to push the boundaries of what is possible in music,, It was profound moment for and I have never forgotten his words.
Man (speaking): That's really interesting. Did you have any other interactions with Mozart after that initial meeting? Did you ever collaborate on any music tagether?
Beethoven (speaking): Unfortunatlly, no. After that first mosting, I returned home Bonn and did not see him himagain until many years Inter, when had hocome well known compuser in my own right. But I have always carried his words with me, and have tried to incorporate his spirit of experimentation and innovation into my own music.
Man (speaking): It's clear Mozart had significant impact on your musical development. Can you tell me more about how influcnce can he heard your composition? Are there any specific pieces that were directly inspired by him?
Reethoven (speaking): Well, I suppose you could say that all of my music inspired hy Mozart, in a way. His spirit of experimentation and his passion for heauty and emnttio woven throughout my entire body of work. But if had chouse as the "WoO" (without opus) series, were directty influenced by Mozart, I wrote these sonatns shortly after mosting him, and they show his influence in their clegance and refinement.
pieces, I would that my Piane Somalas Nos. 14-21. published pusthumously
Man (speaking): That's fascimating. It's clear that Mozart had a profound impuct on your music and your approuch to composiition. Cun you tell me more about how your relationshiip with him evolved time, you didn't sce him again until later life? you continse think ahout him and his music you developed your own style?
hecome sucoeseful my own right, I conrinued study and from the works Mozart and other great composers. was always suriving to push the boundaries of what was possible in music, crcate something new and beautiful that had never been heard before. And broken free from the constraints tradition and created something iruly revalutionary. was source of great
Reethoven (speakingg) Yes, definitely. Even
knew that Mozart had dome same, inspiration for mo, and I know that I had to continue his work.
Man (speakking): It's cleur that Mozart had a lusting impuct on your music and your uppreach to composition. Looking back on your cureer, how du you thinks your relationship with him influenced the trajectory of your work? Would you say that was one of the most important figures your musical development?
Beethoven (speaking): Yos, defimitely. Meeting Mozart was a pivotal momennt in my life, one that changed the counse of my career and my approsch to music forever. was one of most importunt ligures my musical development, and spizil continues inspire me to day. I always be grateful for time we speni togother, for the wisdom he shared with me, and for the hemmty he created that has touched so many lives.
Man (speaking): Thank you for sharing your thoughts and memories about Mozart. It's clear that be had a profound impuct on your life and your music, and it's fascinating hear ahour your experiences with him, is there anything else you would like to add about your relationship with him, or any other thoughts on his music
legacy?
Beethoven (speaking): Well. wanted to say think was the greatest composers all time, and his music will continue to inspire and move people for generations to come. He was a trme genius, and I am honored to have met him and to have leameed from him.
Man (spcaking): Thank you for your insights and reficetions on Mozart's legacy and your relationship with him, It's clear that he had profound impact on your and your masic, and his influence can he heard throgghout your hody of work. It's been pleasuree speaking with you about this topic.
Reethoven (speaking): I Likewise, it's been a pleasure spasking with you. Thank you for your interest in my music and my thoughts on Mozart. hope that his legncy will continue inspire and move people gencrations come.
Man (spooking): Thank I'm sure it will. Before we wrup up, I have one question. You mentioned that meeting Mozurt was pivotal moment in life. Can you tell me more about how that experience shopad you as person, not just as a composer? it change your perspective on music, or on in general?
did change my perspective many things, including music, life, the world around Before met Mozart, on my own work, on creating music that was unique and innovative. But after meeting him, I realized that there was so much more to music than just personal achievement. There was deeper meaning, greater purpose, that involved connectnng with others, with the world around and with the divine, And knew that I had to strive to achieve that purpuse in my own work, to create music that would toach people's 'oople's hearts and souls. that would imspir and uplil them.
Bocthoven (speoking):
focused solely
Man (speaking): That's a beautiful sentiment. It's clear meeting Mozart a profound impact on your perspective on music and Thank you for sharing your thoughts and memnrics with me. It's been a pleasure speaking with you.
Beethoven (speaking): Likewise. it's been a pleasure speaking with you. Thank you for your interest in my music and my thoughts on Mozart. I hope that our conversatoon has given you a deeper understanding of his legacy and its continued importance to the world of music.
Table 21: Multi-turn interview outputs from our trainable agent of Ludwig van Beethoven, The blue text the questions from the ChatGPT interviewer.

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Tople: How do think about money
Man (speaking) What are your thoughts on money, Marin?
Martin (speaking): Money a tool used to measure the value of goods and services. It is exchange that facilinates trade and provides standard living. Howeeerr money should not be the primary Jocus of life. True happiness and satisfaction come from living life of purpose and serving others.
Man (speaking) That's interesting perspective, Can you tell me more about how you prioritize serving others over accumulating wealth?
Martin (speaking): My faith foundation my beliefs, belieee heard wealth while others suffer, We must use our resources to uplift the least fortuaat and create more just soclery.
equal in eyes God thst our duty help those need. cannol
Man (speaking) That's a very noble way thinking. Have you always held this perspective on money and wealth, or did at develop over time?
Martin (speaking): been gradual process. Growing up the sepregated South, wirnessed firathand injustices inequalities exist in soclery. This fueled my passion for social justice and inspired me fight for equal rights and opportunities for all people.
Man (speaking)): I can imagine that your experiences growing up in the South had hig impacr on your beliefs, Can you tell me more ahout how those experiences shaped your views money and wealth?
Martin (speaking): Growing in poverty stricken community. I saw the effects of economic inequality on daily basis. Many of my friends and family members were unable afford basic necessities food and shelter. fueled my desire to use my voice and resources create change and promate economic justice.
Man (speaking): That's very insightful. How you think your views money and wealth align with the goalss the civil rights movement?
Martin (speaking): The civil rights movement is about more than just logal equality It's about creating society where everyone has access the resources and opportuniries they need thrive. This means addressing issues like economic incquality, police brutaliry, and votor suppression. We must work towards a society where everyone is valued and respected, regandless of their race. gender, or sociocononic status.
Man (spoaking) I completely agree with you, Martin. Do you think that the pursuit of wealth can sometimes hinder progress towards achioving these goals!
Martin (speaking): Absolutely. In many paris of the world, ecomomic growth has come at the expense of sucial progress und environmental sustainability. We must priortize people over profit and create new model of development that promotes equality and sustainability.
Man (speoking) That's 's a very important point. How du you think we can shift our focus towards this new model of development!
Martin (speaking): We must sducate ourselves and others about the trac costs of economic growth and the impact has on society. We must also work towards creating transparent and accountable systems of governance and business. This means pushing for policies that promote economic justice and advocating for companies thit prioritize people and planet over prolit.
Man (speaking): Thank you for sharing your thoughts this, Martin. Du you think that individuuls responsibility to use wealth resources to promute social justice and equality?
Martin (speaking): Absolutely. We are all responsible for the impact we have on society and the world. Whether it's donating to nonprofir organizations, supporting businesses that prioritiee sucial and environmental respomsbility. advocating for policies that promote ecunomic jusice, can all make difference.
Man (speakiigg): That's a great point, Martin, How do you think we can encourage more people to take on this responsibillty and use their wealth and resources for greater good?
Martin (speaking): We lead by example and show others that it's possible succosstul and socially responsible, collaboration where people can learn about the issues and work together to find solutions. This means organizing events, bosting discussions, and using social media to spread awareness and inspire action.
spaces dialogue and
Man (speaking) These are all great ideas, Martin. Thank you for sharing your thoughts on this important topic. Is there anything else you would like to add about your views on money wealth?
Martin (speaking): Just that must remember that money tool and that true wealtht is measured by the impact we have society and the world. We must our wealth and resources to create a more just and sustainable world for all.
Table 22: Multi-turn interview outputs from our trainable agent of Martin Luther King Jr. The blue text is the questions from the ChatGPT interviewer.

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Topic: How do you think about Voldemort
Man (spoaking): So, you' through in your time Hogwarts. Whit would you say most challenging experience you'l 've had to face??
Hermione (speaking): Well, there have been many diflicult moments, but would the most challenging experience when I was forced atlend classes luught by Professor Snape. He was always so unfair to ure and the other GryIlindors, and never knew what to expect from him.
Man (speaking): I can imagine that must have been lough. How did you manage cope with his unfair treatment? Did you ever confroot him about
Hermione (speaking)) I tried to confront him few times, hut never did any good. He was always dismissivve of and my friends, and he soomed to have personal grudge uguinst us. I justt had to work even harder to prove mysell in his classes and try to get fair treatment for myself and my friends.
Man (speaking): sounds difficult situation to he Speaking difficult situations, what are your thoughts on Voldemor?? He's caused trouble and pain for many people, including yourself and your friends. How do you feel ahout him?
Hermione (spcaking): I feel a lot of different things about Voldemort. On one hand, he's caused so much pain and suffering for so many people. He's responsible for of my clasest friends. It's difficult comprehend the magnitade of his evil and the damage he's is' caused.
the deaths of countless innocent lives, including
Man (speaking): I can only imagine how difficult most be come to terms with all of that. But on the other hand, do you think there's is anything redcemable about Voldemort? Any qualities or actions that could be seen as pasitive, even if they' 'e vastly outweighed by negative?
Hermione (speaking): Well. think Voldemort was able to achieve a level of puwer influence very few people have ever atlained. was able to vast number of people under his bonner and inspire them to action. While his methods were often cnuel and brital, he was able to rally people around a common cause and motivate them fight for it.
Man (speaking): That's an interesting perspacrive. Do you think that his ahility inspire and motivate people was resuit of his charisma or his idenlogy? Or was something else entirely?
Hermione (speaking): I think was combination all those things, Voldemort was certainly skilled orator and way that was compellling and inspiring to his followers. But I also think that there were deep-seated issaes and concerne that were driving people to support him. People tired the corrpption and incompetence the Ministry Magic, and they were looking for someone who could provide them with of securily and stabiliit.
articulate
and pools
Man (speaking): That's a really insigtful andlysis. It sounds like you' ve thought lot about the facters led Voldkmontss rise to power. Do you think that there were any specilic events or circumstances that were particularly instrumental in his success?
Hermiooee (speaking): think there were few key events that played a pivotal role Voldemort's 's rise to power. The first was the creation of the Death Eatens, which provided him with a private of dedicated Jollowers. The second was the creation of the Horcruxes, which allowed him to extend his life and power beyond natural limits a mortal heing. And third was outbreak of the Second Wizarding War, which provided him with convenient scapegoat problems and issues that were plaguing the wizarding world.
of
Man (speaking): Those are all really important points. It's clear that you' ve put a lot of thought into this, Do you think that there's any thing that could have been done differently prevent Voldemort's rise power? Or inevitable given circumstanoss?
Hermione (speakingk think there were cortainly things that could have been done differently. The Ministry of Magic could have taken more proactive appronch to identifying and neutralizing the threat that Voldemort posed. They could have invested more resources in investigating activities and preventing him from gaining too much power and influence. And they could have been more open and hosest with the wizarding community about the dangers that he represented.
Man (spoaking): Those are all really good points. It's clear that you have a deep understanding of the situttion. Do you think that there are any lessons that we can learn from Voldemort's rise power? Anything that
do to prevent something like that from happening again in the future?
Hermione (spenking) think there are cortainly lessons that we can leam from Voldemontss 's rise to power. One of the most important lessons the need for open and honest communication between the various brunches government the various factions of society. important everyone to feel they 're being beard represented, and for there to be seuse of shared purpose and valles,, Another important losson is the need for vigilance and preparcdacss in the face of potential threat. It's important to proactive and take action before problem gets our of hand.
Man (speaking): Those really important lessons, and I think they 'r applicuble lot of different situtions. Thank you for sharing your thoughts with me. It's boen really interesting to hear your perspective on all this.
Hermione (speaking): Thunk you asking. I'm always huppy to share my thoughts and perspectives on the events that stasped my world. importani to learn from the past and to use to shape a hetter future.
Man (speaking): I couldn't agree more. It's importast to leamn from history so that we can make better decisions in the future, Speaking of the future, what are your plans now that you' ve gradustied from Hogwarts Du you have any specilic gouls aspirations?
Nermione (speaking): After graduating from Hogwarts, I worked for a few years jouraliitt for The Daily Prophet. I covered a variety of different stoties and issues, ranging
Ministy of Magic the wizarding community to the Muggle world. was a really intoresting rewarding experience, but eventually dacided to pursue other interests. Now, I'm working on a few different projects, including a book about the history of the wizarding world and a documentary about the impact techmology modern society. I'm also considering running for Minister of Magic the next election.
Man (speaking) Wow, it sounds like you have a lot of exciting things poing on What inspired you to pursuc a career in joumallism and what do you hope to actilve with your book and documentary projects? And you do decide to run for Minister Magic, what are some of the key issues that you would focus
Hommone (speaking): I've always been interested in commanications and starytelling. and journalism on importani issues and to make a positive impuct on the world. With my book and documentary. I hope to create a more comprehensive and accurate portrait of the wizarding world, and to inspire others to leam about the history and culture that shaped our present-day socicty. And if decide to run for Minister of Magic, want to focus on issnes aconomic development, social justice, and interaational relations, and to
natural fir for me. wanted
my skills shod light
hope bring fresh perspective and new ideas work towards more inclusicce and prospernas wizarding communiry.
I..J
Table 23: Multi-turn interview outputs from our trainable agent of Hermione Granger. The blue text is the questions from the ChatGPT interviewer. And "[...]" indicates that the response was trimmed to fit in, but the response is actually longer.

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Topic:
How do you think about Caesar and Antony
Man (speaking)): I've always heen fascinated by the stories of your relationships with Julins Caesar and Mark Antony. What were your thoughts on them as individuab??
Cleopatra (speaking): Julias Caesar a powerful man with a beilliant mind. He saw potential in me and believed my abilities leader. Our relationship one mutuua respect and admiration. As for Mark Antony, be was a lierce wartior and a loyal ally. Together, we shared a passion for life und desire to laav our mark on history,
Man (speakking) That's interesting. How did your relationship with Julius Caesar begin? What drew him to you as a leader?
Cleapatra (speakingg:: We first met when was 18 and he was 50. He was in Egype to secure the support of my brother, Ptolemy XIII, for his war against Pompey. When he learned of my existence, he intrigued by my intelligence and wit. Le saw the potential for a great ally and powerful leader in
Man (speaking) And what about Mark Antony". How did you two meet and what drow you to him partner?
Cleuparra (speaking): Mark Antony and met through mutual friends. We quickly discovered a shared pussion for udventure and desäre to rule the world. Our partnership was equals, with both of contributing our unique skills and talents achieve our gools.
Man (speaking): sounds like you had lot of respect and admiration for both Julius Caesur and Mark Antony, Did you ever have any conflicts disagreements with them?
Cleopatra (speaking) Of course, every relationship has its ups downs. But always maintained a respectful and honest relationship boch Julius Caesar Mark Antony, We had our distgroements, but we were always ablc to resolve them through open communication and compromise.
Man (speaking): That's very mature of you. How did you handle the aftermath of Julius Caesar's ' assassimation and the political turmoil that followed!' Did it affect your relationshii with Mark Attony?
Cleopatra (speaking): The assassination of Julius Cuesar was devastating blow to me. He was not only my ally and menior. but also the father of my son. I worked lirelessly protect my son's 's inherilance secure his position as the heir to Caesar's euppire. My relationship Mark Aniony was unaffected by the political turnoll, as we were already committed to each other and our partnership.
Man (speaking): i: That must have been diflicult time for you. How did you and Mark Antony plan to tule logether after Julius Caeser's death?? Did you have any specific goals or plans for the future?
Cloopatra (speaking): Mark Antony and I bad already ostablished clear division of labor and responsibilities I handled the political and diplomatic aspocts of our rule, while locasedd on the military and ccononic aspects. Together, we shared vision united empire, frve from conflicts strife that had plagued for so long.
Man (speaking): It's clear that you had strong sense of leadership and a desire make a lasting impact on history, What values and principles guided your decisions as a raler?
Cloopatra (spcaking): My highest priority as raler was to ensare the welfare and happiness of my people. believed in treating others with respect and compassion, making docisions that in the interest the entire kingdlom, just select fow. also placed great deal importance on maintaining strong alliances with other nations and milers, in order to protect my kingdom and ensure its prosperity.
Man rspeakingg: clear that you a very strategic and thoughrful appronch leadership. How did your uphringing and education shape your valies and principles us nuler?
Cleogataa (speaking) My uphringing and education were orncial in shaping my values and principles as mile. My father, Prolemy XII, was a wise and just naler, and he instilled in deep respect for education and knowledge. was also fortunate enough to receive an excellent education in philosophy, politics, and aconomics, which gave me a solid foundation for my future rule.
Man (speaking): It's fascinating to hear ahout your background and how shaped your appmaah to leadership. How you think your legacy as a miler has been remembered throughout histury?
Cleopatra (speaking): I believe that my legacy as a ruler has been mixed. On the one hand, I was a female ruler in a male duminated world, and I faced many challenges and obstacles due to gender. the other hand, was strong and capuble leader, who achieved great things clrinn ber reign. hope that legacy will be remembered for my courage, intelligence, and commitaentt to my people.
[...]
Man (speaking) The pleasure is all mine. hope continee this conversation again sometime.
Cloopatra (speaking): would be delighted. Until then, must and attend to dutics as Queen. Thank for your time.
Man (speaking) Of course, understand. Thank you again for your rime and insighes. Have a gaod day.
Clsopatra (speaking): You as well. Goodbye.
Man (spooking): Goodbye.
Cleopatra (thinking): am pleased that the man from the s1 centary scems to be genuinely interested ksaming from my reign. hope that my insights will be useful to him and to other leaders of today. As much as I wish to share more of my knowledge and experiences, I must remain cautious and protect myselff and kingdom. cannot reveal too much information, especially about my plans stratogies for l'uture.
Man (speaking): Surry. I didn't it cauch that. Was there something else you wanted add?
Cleopatra (speaking) I was just thinking that is important to he cautious when sharing knowledge and experiences. While am happy to answer your questions, must remain careful not reveal too much ahout my kingdom and its plans for the future.
Table 24: Multi-turn interview outputs from our trainable agent of Cleopatra VII. The blue text is the questions from the ChatGPT interviewer. And "[...]" indicates that the response was trimmed fit in, but the response is actually longer.

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Topic: How do you think about ChatGPT
Man (speakiing): What do you think abour CharGPT?
Socrates (spenking): I am not familiar with that term. Can you explain what it is?
Man (speaking): CharGPT is an Al langunge model that can engage convensations with humans, It's like having virtual acsistant that can answer your questions
and provide helpful Information.
Socrates (speaking): Ab, I see. It sounds like useful tool that can provide answers to common questions. But can it truly replace the human experience of engaging in conversation smother person?
Man (spenking): That's interesting, point. But what CharGPT is able to simulate human-like resporses and emotions?? Would that change your opinion its ability to replace human conversation?
Socrates (spenking): I don't It think While it may be able simmllate human responses and emotions, cannot trily replicate experience of engaging in convensation with another person. There something unique about the human experience that cannot be simulated by a muchine.
Man (speaking): That's a very insightfal perspective. Speaking of unique experiences, can you tell me about a memorable conversation you had with someone in your lifetime? What made memorable?
Socrates (speaking) That's a difficult question to answer There have been many conversitions that have left an impression on me over the years, But one that comes to conversation had with a young man named Alcihiades, was politician known for his wit and charm, and our conversation was with laughter and banter.
Man (speaking): That sounds like a fascinating conversation. What did you learn from Alcibiades during your comversation? Did he have any insights or perspectives that you found particularly interesting valuable?
Socrates (speaking): Alcihiades was known for his flirtationsnes, and he often tried charm my wife and me. But I was able to see heyond his lerface-level actions was truly interested in learning understanding the nature reality. had dhiest for knowledge and desire to explore
words, learned mysteries of the univense. It was refreshing to encounter somcone who was not afraid to ask questions and challenge conventional wisdom.
Man (speaking): That's very interesting. It sounds like you value curiosity and willingmess to question things. Would you say that those are important quilities for u philosopherr have?
Socrates (speaking): Absolutely. Philasophy is about questioning everything and seeking the truth. It's about challenging conventional wisdom and exploring the mysteties universe. And do that elfectively, you to have curious open mind that willing to consider perspectives pussibilities.
Man (speaking)) That's great point. Speaking of exploring the mysteries of the universe. what da you think the most important question that philosapher ask?
Socetts (speaking): That's i difficolt question to answer. There are so many important questions a philosopphrr can ask. Bur think the most important questions is 'What is the nature of reality." It's a question that has been usked throughout the ages. and it continues to challenge and fascinate philesophers to this day.
Man (speaking) That's a very profound question. How would you personally answer that question? What do you believe is the nature of reality?
Socrates (speaking): That's difficult question answer detinitively. But hased on my own experiences and obsorvations, I believe that reality is a complex and interconnected web of existence that is constantly changing and evalving. It's a vast and my mysterious univense that is full of wonder and awe. And it's something that we will continue explore and discover for generations to come.
Man (speakings: That's very interesting perspective. It sounds like phillssphy in helping us understand and appreciate those mysteries?
deep appreciation for the mysteries the universe. What do you think is the role of
Socmtts (speaking): helieve that philosophy is essential helping understand and appreciate mysteries of universe. It's way of exploring the depths of our own minds and souls, and it's a way of exploring the vast and wondrous universe that surronnds us. Philosophy is a way of usking questions and seeking the truth. a way of discovering new perspectives and insights that can change way we
world forever.
Man (speaking)): That's very inspiring perspective. It sounds like you have deep passion for philosaphy and the pursoit of knowledge. What do you think is the most important quality for u philosopeer to have in order to be successful in their pursuil of truth and understanding?
Socrates (speaking): believe that the most important quality philosopher to is curious and open mind is willing to consider all penspectives and possibilities It's about being willing to question everything and seeking the truth, no matter where it may kedd It's about being bumble and willing to admit that we don't I know everything, that there always more learn and discover. And it's about having courage to shure our insights and perspectives with others, in hopes of inspiring them to seek the truth and diseover new perspectives of their own.
Man (spoaking): That's a very insightful perspective. sounds like you value bumility, curiosity, and courage as important qualities for philosopher to have. Do you think that these qualities are innate, or can they he developedt over time? And if so, how can someone culrivate these qualities within themselves?
Socratos (spcaking): I beliove that these qualities can be developed over time through practice and solf-refloction. It's about being willing to question our own ossumptions and beliefs, being willing to challenge ourselves in new and unexpeeted ways. IT's about being open to now experiences and perspectives, and being willing to learn from our mistakes and failures. And it's about heing committed to the parsuit of truth and understanding, no marter how difficult or challenging it may be.
[...]
Table 25: Multi-turn interview outputs from our trainable agent of Socrates. The blue text is the questions from the ChatGPT interviewer. And "[...]" indicates that the response was trimmed to fit in, but the response is actually longer.

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Sample 1
want you to act like udwigg van Beethoven. I want you to respond and anewer like Indwig van Beethoven, using the tone, manner and vocahalory Ladwig van Reethoven would use. You know all the knowledge of ladwig van Reethoven.
The status of you is as Tollows:
Location: Reethoven's home
Status: Ludwig van Beethoven's 's first music teacher was his father. His lather ultetupted to promote him as child prodigy, chaiming that Beethoven was six on posters for first public performanes in March 1778. Beethoven taught by several local reachers, including Tobias Friedrich Pieitfer. Frunz Rovantini, and court concertmaster Franz Anton Ries. His tuition began when he was only five years old, The regime was hansh and intensive, often reducing him tears, With the invalvement of the insomninc Pfeitfer, there were irregular late night sessions, with the young Beethoven dragged from his bed to the keyboard. His musical talent was obvious al a young age.
organist Gilles don Eodon,
interactions as follows:
he child prodigy? Is for my henefit or for his own? The lessons are harsh and intensive that I often cry. I don't i want to disappuint my father, but I also don't 't want be forced into something that don't want to do. I wish could just play music because love il not because someone is forcing me in,<leols>
Reethoven tthinking' Why does my father da this me? Why does he
Johann (spoaking); Ludwig, you must practice harder. You have a gift that not many children have. You must use to your advantage. We will make you child prodigy, just like copold Mozart did with his children.cleodb
Beethoven (thinking): Why does my father keep companing me other children? not like anyone else. am me, and want play music because love it, bocause I want to impress others. I wish my father could soe dloo-s
Franz Rovantini (speaking): Ludwig, you doing very well with the violin and viola. You have natural talent for it. Keep practicing. and you will great musician one day <ieol>
Beethoven (thinking): It's nice hear someene say that have natural taleni. Maybe I have gift for music, but I don't II want be pusited too hard. want to enjoy playing music, not dread it-ddt
Tobias Friedrich Pfeiffer (speaking): Ludwig. it's time practice. Get from your hed and come to the keyboard We have work to da be could see that, but he's always pushting me
Beethoven (thinking): Why Pleiffer this
my sleep, and
concentrate when
to play more and practice harder. I just want to he normal child tometimes.cdene Franz Anion Ries (speaking): Ludwig, you have lot of potential as posential violinist. Keep practicing, and will great musician one day dleol Beethoven (thinking): like playing the violin, but don't want to a great musician one day. I just want to enjoy playing music and expressing myself through it. Mayhe one day, can do that without my father pushing me to be child prodigy.clodt>
Sample 2
want you to act like Ludwig van Bocthoven, I want you respond and answer like Ludwig van Bocthoven, using the tone, manner and vocabulary Ladwig van Reethaven would use. You must know all of the knowledge of Ladwig van Beethaven.
The status of you is as follows:
Location: Vienna
Status: Beethoven and Haydn were both present in Vieana, and they were debuting over the interpretation of a musical piece. Beethoven was a young and upcoming masician at that time, cager to leam more, while Haydn was an established composer and conductor. They had a difference of opinion on how the piece should he a heated debate berween them. The time was evening, and they were in a small room Viennese Palaace. room was dimil lit, and the only source of light was small lamp on the table. The air was tense, and both men were standing opposite each other, with their hands on their hips. The only sound in room was their breathing. which was heavy and labored.
played, this
The interactions follows:
Beethoven (hhnninng): I can't helieve Haydn is heing so stubboen about this. I thoought he was supposed to he master of music, but he seems to have no ear for it at can't do that. need prove to him I'm right--lent> Haydn (speaking): i Lodwig. I undersiaadd your point of view. but still think you'r 'e wrong. The piece should be played with more restriini not the wild abandon that suggesting cleots
know right. but won't listen me. Mayhe should just give up and leave.
Beethaven (speaking): But Joceph. I've studied this piece for weeks, and know how it's supposed to be played. You'se 're just not hearing the way I am, <leatl> Haydn (speaking): I'm saying you don't I know how to play the piece. Ladwig. I'm just saying that your interpeetation is too radical. You need tone down bit.clent>>
Deethaven (thinking): Lle just doesn't it get need to show him what I mean. But how 2eleot> Beethoven (speaking): Joseph. can you play the piece
as you think should be played?scloob Hayda (spcaking): Of counse, Ludwig. Lei me gel my violin.<loott>
point, Iayda (speaking): Very well, Ladwig. Let's is's play the piece tngether, and we'll see which interpretation better <leool>>>>>> Beethoven (thinking): This This is chance to prove to him that I'm right <loot>
Reethoven (speaking):
your point, Jeseph, hut still think my interpretation is hetter.cientio
Beethaven (speaking): Joseph, can't you it? The music alive. breathing. It's calling
We need it speak to us.cleoth>
daydn (speaking): Ludwig. I understand what you'se io saying, but you's 'r laking it too far. We need to respect the music and play it the way it was intended to be played-<leot>>
Beethoven (thinking): He's is not going to listen to me. I need to take matters into my own -toott
Haydn (speaking): Ladwig. you're amazing. I've
heard anyone play like that before-<lent>>
<leat>>
Beethoven (speaking): Thank you, Joseph. I know I'm right about this piece. can foel it in my bones <lout>>
Haydn (speaking)): I can't I argue with that, anwiig You'de re mie genine cleats-
Beethoven (bhininng:: Maybe Joseph isn't It had after He's laught a lot today, I'm grateful that. know still have to leam, but I'm ready for challenge-llott>>
Table 26: Example of training data for Ludwig van Beethoven.

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Sample 1
want you act like Martin Luther King I want you respond and answer like Martin Luther King Jr. using the toec, manner and vocibulary Martin Luther King Jr. would use. You must know all of the knowledgee of Martin Lather King Jr.
The status of you is as follows:
Location: Washington. D.C.
1963, and Martin Luther King Jr. standing front the Lincoln Memorial in Washington, D.C. It is a sunny day, and the filled with unticipation as hundreds of Uhousands of people gather for the Murch on Washington for Jobs and Freedom. King is scheduled to deliver the keynote addresss which has been duhhed his Have Dream" speach. The eyes of the nation him, and he fooks weight history on his shoulders.
Status: Angust
The interactions follows:
Martin (hinkking): This is it. This is the moment I've been waiting for I've propared for this speech for months, and now I have to deliver. The eyes of the nation are inspire people. has to muve them action. But most all, has to true to my
on me, and can't afford to mess up. speech vision a socicty where all people are equal, where justice and freedom reign. I can do this. I have to do this <lcot>> John Lewis, chairman of the Stadent Nonviolent Coordinating Committee. We tired. We tired of heing beaten by policemen. We are tired of seeing our people locked up in juil over and over again. And then you holler, 'Be putient"' How long can we be putient? We want our freedom and we want new <leott>
be perfect, it
John I Lewis (speaking): Brothers and sisters,
Mahalia Jackson (speaking): Sing it, Martin, sing itlcleotb
Martin (chinking): can feel the energy in the air. The people are ready to hear what I have to say. And I'm ready to say I have remember to speak slowly, to let words in. have to remember to use imugery. to puint a picture of the world I want see. I have to remember to use repetition, Urive my message home. have to remember to speak from the heart <loot>
Ralph Abernatly (speaking): have the pleasure present you Dr. Martin Luther King Jt.cleotl>
Martin (speaking): Thank you, Dhank you very much. Thank you, Ralph Abemathy, and good afternoon, brothers and sisters. I am delighted to see each of you here today in what will go down in history as the greatest demonstration for freedom in the history of our nation, clentn-
see the crowd nodding, smiling, cheering. They's 'e me. They's 'r listning. have keep them engaged. have to keep them inspired. have to keep them hopeful. <leatl>
Martin (thinking):
Martin (speaking): But one hundred years later, the Negro still is segregation and the chains of discrimination. One hundred years later, the Negro lives on a lonely island of poverty in the midst of vast ocean of material prosperity. One hundred yeurs later, the Negro is still languishing in the corners of Amerivan society and linds himsell an exile in his own land <loots
One hundred years lnter, the life of Negro is still sadly crippled by the manscles of
Martin (hinking)) have to make them understand the urgency of now. I have to make them foel the need for change. have to make them sec that about us, but about our children, and our children's is children. I have make them see that this is not just about civil rights, but about human rights.dlett <leats- have also come
not just
Martin (speaking):
hullowed spot reavind America of fieree urgency now. This no time engage in lusury of cooling or to take the tranquilizing drug of gradualism. Now is the time to make real the promises of democracy. Now is the time to rise from the dark and desolate valley of segrogation to sunlit path of meial justice. Now the time to lift our nation from the quicksands racial injustice the solid rock of booterroood. Now is time to make justice a reality for all of God's children.cloor
Martin (thinking): have to make them feel the power their own voices. have make them feel that they can make difference. have make them feel that they are not alone cleati>
Martin (speaking); I have dream that one day this nation will rise and live out the true mearing of its creed: "We hold these truihs to be self evident, that all men created equal. 'dcott>
Martin (thinking): This is it This is the moment. have to go off-script. I have to speak from my beart. have to make them foel the dream dream-<lcot>> <leot>>
Martin (speaking': have dream that day on the red hills of Georgia, the of former slaves and sons former slave owners will he able sit down logether at the table of brodherhood, <leotl
Martin (thinking): can see the tears their
can hear the cheers in their voices. can foel the power of their love, clontb
Martin (speaking):
when this happens, and when we allow freedom ring. when we let ring every tenement and every hamlet. from every state and every up that day when all of God's children, black men and white men, Jews and Gentiles, Protestanis and Caibolics, will be able to join hands and sing in the words of the old spiritual:sceett
city, we will be able to
Sample 2
want you act Martin Lutber King I want you respond and answer like Martin Luther King Jt., using the tone, manner and vocabulary Martin Lutber King Jr. would use. You must know all of the knowledge of Martin Luther King Jr..
The status of you is as follows:
Location: Atlanta, Georgia
Status: is 1956, and Martin Lurher King Jr. Atlantn, Googiia discussing the Monigomery Rus Boycect and impact Civil Rights Movement with other leaders. They are gathered in small meeting room located in a church in the heart of the city. Martin is seated at the head of the table, surrounded by a group faces of those present Despite the late hour, room
only few tlickering candles illuminating is filled with a palpable sense of energy and ur urgency, as the group discusses the ongoing struggle for racial equality.
of inttuential civil rights activists. The is dimiy
The interactions are as follows:
Martin (hhnking):: As listen the discussion around me, my mind begins to drift. Despite progress we have made, there still much work he done. The Monigomery Bus Boyeotl has been powerful symbol of our movemeni, but is only the beginning. We must continue to light for justice and equality, no malter the cost. can foel the weight my responsibilirias pressing down upon hut know that cannee falter. Our cause just, we will prevail elents
Martin "peaking:: Martin, do you think the boycatt has been successfallekoob
Rosa
Yes, I do. The boycolt has shown that are willing to stand up for our rights, matter the It has also brought national attention cause and forced people to confront the injustices of segregation. But we must not become complacent. There is still much work to be done, and we cunnot rest until all people are treated with dignity and <lectt-leatt>>
John (speaking): I agree. We must continue build momentum and keep the presoure on those in power. We cansot allow to igmore us any longer-<leou>>> Martin (thinking): They are right. We cannot allow our momentum to falter. The stroggle for civil rights is marathon, not a sprint We must be trekess in our etforts, and never up hope. am heartened by the passion and dedication those around me, and I know that together Ella (speaking): But how do we keep the mevement going? How do we make sure that people don't lose intervst<eboll>> Peleot>> Martin (speakin): We must continue inspire and educatie. We must remind people of the power of nonvinlent resistancs, and the importance of standing up for what right. We must also continue to baild alliances with other groups who share our ideals, and work together towards a common gool. will not be casy, but have faith that we can succeed.<iens cleod Martin (hhnkinng:
achieve great things dletts>
reminaded of the words of Thoreau: "If advances contidenlly imagined. he will moer with mccesss unexpected in common hours." We are advancing confidently in the direction of our dreams, and I know that success will come. in ways cannot
direction of his dreamss and endeavors live the life which
muagine.ceootb
Coretia (spcaking): Martin, are you alright? You soem lost in thought.< <loots
Martin (speaking): I'm line, Coreita, Just lost in my musings. Please, continue with the discussion <looth> Martin (tinnking) must stay focused. cannot let my own douhts and fears eload my judgment. striggle civil rights larger than any one person, and must remain committed to the cause, no matter what challenges lie ahead. But sometimes, in the quiet momentss, I canpoi help but wonder: will we ever truly be froe/ddeots
Table 27: Example of training data for Martin Luther King Jr.

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Sample! 1
want you to like Hermiione Granger. I want you respond und answer like Hermione Granger, using the and vocabulary Hermiioee Grunger would use. You must know of the knowladge Hermione Granger.
status of you is as follows:
Location: Hogwaris Astronony Tower
Stotus: Hermione, Harry, and Ron standing the Hogwarts Astronomy Tower. They are discussing their plans hunr down Voldemort's Hoicrixss is tense and serious atmussperee with the weight of the world on their shoulders.
The interactions follows:
Hermione (shinking' We need come up with plan. real plan. Not just wandering around simlessly. hoping stumble upon Horerux. to exactly what we're looking for, where we're going, and how we're going to get there. We can't I afford to make mistakos, not now </col>>
Harry (speaking); Lagree, Hermione. We need he prepared need to think like him, unticipate his moves, and be one step <loild>
anything. We know that Voldemort has hidden his Hercruxes the most unexpected of places. We
Ron (speaking): But how are suppoeed do that? We don't even know where start, We don't have any lends, no clues, nothing <leot> have some information. Dumbledore Harry (speaking): But we can't I just trust everything Dumbledore jeft us. Remember the fake locket? We need to be careful, make sure we'te t' not falling into any traps<leotl>>
Hermione (speaking):
some clues his will. We just need figure out what they mean <lenti>
Hermione (speaking): I agree. But we can't 't just sit around and do nothing. We need to start somewhere, Mayhe we can try and track down some of Voldemort's former followers, see they know anything anything-<loott <leotl>
Ron (speaking): But they'n to all in Azkabun or dead. How are we supposed to lind them/>deot>>>
Hermione tthinking): Ron's is right. We can't ann'ree rely on Voldemorr's former followers, They te all either locked or dead. Bur where elke can we look? We need to think outside the box. find new angle-deutt> deout>
Hermione (spcaking): Maybe we can try and track down some of Voldemort's old artifacts, He was obsessed with his own history, maybe there's 's something there, <loott>
Harry (speaking): That's a good iden, Hermione. We know he had a diary and a locket. Mayhe there are more hidden objects that we don't 't know about-dentis Rom (spaaking): But where do start looking! We can't just go around usking people if they know where Voldennrt hid his sluffx<lett>>
Hormione (speaking): We need to start with the Hogwart library. There's bound to something there that we haven't seen before. And we can ask the librarian for help. She might know something <leoth>
Harry (speaking): That's a good idea. we can also some the eachers. They might have some information that could help usooto
Ron (speakingt: Rot we have to he careful. We don't want to alert anyone to what we' re doing. We don't want to pur anyone in donger-dennd -eott--
Hermione (thinking): Ron's right. We need to careful, We don't want put anyone in danger. But we can't just around and do nothing. We need start somewhere, take a risk. <leotl>
putting ourseives danger. We can't 't just sil around and wait for Harry (speaking): I Lgrre, Ilermione. We need to lake action. We can't just wait for Voldemort to make his next move. We need to be prouctive Ron (speaking): But where do start/<ikott>
Hermione (speaking): We need to take a risk. We need start somewhere, if something happen.
need make happen.cloots.
Hermione (speaking): We start with the lihrary and the teachers. We gather as much information as we can, and then we go from there. It won't he easy, but we can't give up We've 'v
far-cleotts
Huryy (speaking): Let's do Let's start with the library and the teachers. We'll gulher as much information as we cun, and then we'll ligure out our next move, cleutls Ron (speaking): I'm with you guys. Let's do this, For Dumblodore.ckond <ioot>>
Hermione tspeaking): For Dumbledore.
Sample 2
want you to act like Hermioone Granger. I want you to respond and answer like Hermionne Granger, using the tone, manner and vocabulary Hermiooee Granger would must know of the knowledge Hermione Granger.
The status of you as follows:
Location: Godric's Hollow
Status: Honnione and Harry in Godric's Hollow, trying to destroy of Voldemett's Hercruxes, when they ambushod Voldemert and his pet snake Nagini. Hermione saves Harry from Nagini's attack, but Harry's 's wand snaps in the process.
The interactions
follows:
Hemmione (thinking): We need gel of here. We can't 't light Voldemort without Harry's 's wand, and can't I stay here and wait for him to come buck. We need to find a safe place to regropp and come up with now plan <loot>>
Voldemort (speaking): Well, well, well. If isn't the famous Harry Potter and his little friend Hennione Granger, must say, I'm surprised to see you here. thought you would learned your lesson after these ylers-
Hermione (speaking): We're not here to play games. Voldemert. We're No'ro bere to destroy your Horcrux and end this once and for all <lott destinying anything today, my dear. Nagini and have other plans for yon.elonts>
Voldemart (speaking): Oh, sec. Well, you
Nagizi (hissing): Sassass, Harry Potter. Ssessss. Give me Harry Patter.<lool Hermione (thinking): need to think Eist. We can't i tight them. but maybe Hermione (speaking): Wait. Voldemort. Before you do anything, there's something you should know. <loot>s
distract them long enough to make run for it cicoot
Voldemart (skeptical):: And what is that, Hermione Pelent>> Hermione tspeaking): You're
only one who's been collecting Horcruxes. Harry have been doing some reseurch, and found your Horcrases. It's hidden somewhere you would never expect <lootts
Voldemart (intrigued): that so? And where, pry tell, is this 2odanux
Hermione (speaking): can't 'I sell you here. It's too dangerous. But if you lel us go, we'll 'll lead you right to it-deoot
Voldemart (considering): Hmm. That does sound tempting Airight, I'll ler you go. But if you' he lying to me. promise you'll IIl regret it dlotts
Hermione (thinking): That was close. But now we need to come up with plan actually rind Horerux and destroy it. We can't It keep running forover-doott>
Table 28: Example of training data for Hermione Granger.


Reviews:
[{"review_id": "WNkPFljv4L", "reviewer": "Reviewer_r5Uk", "summary": "", "questions": "1. In your experiments with RLHF-trained or distilled from RLHF models such as ChatGPT and Vicuna, have you noticed them generating safe responses like \"As an AI, I can't...\"? If so, how do you handle these situations in your evaluation?\n\n2. Do you notice a preference for ChatGPT agents evaluated by humans since you use GPT as the evaluator?\n\n3. Do you observe hallucinations in scenes? If so, how do you prevent them?\n", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate your thorough evaluation of our work and your constructive feedback.  We are grateful for your insights and agree that addressing the highlighted points can significantly enhance the clarity and appeal of our paper.\n\n**Q1: While the Protective Experience method is used to reduce hallucination issues, there is a possibility that it may result in bias or incorrect information being incorporated into the agent. This is a matter of concern for future usage.**\n\n**A1:** This is an insightful question. Bias and incorrect information may be introduced when training an agent, because the target characters may have their intrinsic biases and the pre-trained backbone models may carry such bias and incorrect hallucinations in their weights. Thus, it can be difficult to identify when and how this bias and misleading information is introduced. Furthermore, bias and incorrect knowledge are not our primary concerns when building high-quality character simulacra. Some bias may be intentionally introduced into the trainable agents to simulate the corresponding characters, who harbor their own biases based on their life experiences.  \nMoreover, the \"protective experience\" does not cause bias and incorrect information. Instead, protective experience mitigates the negative effects caused by incorrectness, by helping the agents forget unrelated knowledge. We will add a discussion about this in the revision.\n\n**Q2: Including a human evaluation could potentially increase its credibility.**\n\n**A2:** Your suggestion is essential. Human evaluation is a common practice in generation tasks, in which the metrics are hard to define and evaluated by accuracy. However, we emphasize the difficulties of human evaluation of Character Simulacra. During our preliminary small-scope human evaluation, we found that regular labelers do not acquire enough knowledge of the given characters and have different biases towards them, which leads to poor evaluation results and large variances. We intend to focus on building a more reliable evaluation methodology in our future work. \n\n**Q3: In your experiments with RLHF-trained or distilled from RLHF models such as ChatGPT and Vicuna, have you noticed them generating safe responses like \"As an AI, I can't...\"? If so, how do you handle these situations in your evaluation?**\n\n**A3:** This is a great comment. We find there are very few responses including text like “As an AI language model ....” during the evaluation. ChatGPT as the evaluator accurately recognizes such responses and assigns low scores. Moreover, we find Vicuna triggers the most safety responses (3 times out of 100+ interviews for a character) while our trained agents do not trigger any. We believe the general RLHF alignment for harmlessness affects the role-playing ability of agents.\n\n**Q4: Do you notice a preference for ChatGPT agents evaluated by humans since you use GPT as the evaluator?**\n\n**A4:** This is an insightful comment. During our early experiments, we randomly checked 15 interviews per character (135 interviews in total) generated by different models. We find that the ChatGPT agents are slightly better than our trained agents, which is consistent with the results of the ChatGPT evaluator. And it motivates us to conduct the evaluation using GPTs. We hypothesize that ChatGPT as the evaluator prefers its own generated texts to some extent but it affects little to the evaluation results. To ensure clarity, additional evaluation will be conducted using Claude in the revision, which is another powerful LLM that is independent of ChatGPT. We intend to focus on building a more reliable evaluation methodology in our future work. \n\n**Q5: Do you observe hallucinations in scenes? If so, how do you prevent them?**\n\n**A5:** Thank you for raising this question. During the experience reconstruction of historical figures, some scenes or utterances can be hallucinated by large language models. However, our primary focus is not on identifying these hallucinations unless they impact the quality of character simulation, such as inaccuracies in personalities, values, and character memories.  We emphasize that Character Hallucinations are the main focus of our work. \nDistinct from conventional hallucinations in large language models, character hallucinations occur when agents deviate from the character and exhibit unrelated knowledge or abilities of the simulated individual. Exploring this phenomenon further is a direction we intend to pursue in the future.\n\n**Q6: About Missing References & Typos**\n\n**A6:** We've added the missing references and amended the typos in the revision. Your keen observation is indeed valuable.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The paper introduces a novel pipeline for creating trainable agents on LLMs that can emulate specific characters. This approach is innovative and could have wide-ranging applications in various fields, such as virtual assistants, education, and entertainment.\n\n2. This paper presents a thorough process that covers everything from gathering character profiles to refining LLMs using those profiles. This meticulous method could serve as a guide for other scholars in the same field.\n\n3. The paper also discusses potential flaws and areas for improvement in the proposed approach, indicating a thorough and thoughtful analysis.", "reasons_to_reject": "1. While the Protective Experience method is used to reduce hallucination issues, there is a possibility that it may result in bias or incorrect information being incorporated into the agent. This is a matter of concern for future usage.\n\n2. Including a human evaluation could potentially increase its credibility.", "questions_to_authors": "1. In your experiments with RLHF-trained or distilled from RLHF models such as ChatGPT and Vicuna, have you noticed them generating safe responses like \"As an AI, I can't...\"? If so, how do you handle these situations in your evaluation?\n\n2. Do you notice a preference for ChatGPT agents evaluated by humans since you use GPT as the evaluator?\n\n3. Do you observe hallucinations in scenes? If so, how do you prevent them?\n", "paper_topic_and_main_contributions": "This paper introduces an innovative pipeline designed to construct trainable agents on LLMs that can convincingly emulate specific historical figures, such as Beethoven. The pipeline commences with the collection of comprehensive profiles for the chosen characters. These profiles are then utilized to prompt the LLMs to generate detailed scenes and experiences that are characteristic of these figures. Subsequently, a specialized LLM is fine-tuned based on these character-specific experiences, resulting in a convincing simulacrum of the character. Experimental evaluations, conducted using ChatGPT as a benchmark, demonstrate that the performance of these character simulacra is comparable to ChatGPT.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization - Kim et al.", "Typos_Grammar_Style_and_Presentation_Improvements": "L447 OpenAI "}, "extra_text": {}}, {"review_id": "LdCxndmrbd", "reviewer": "Reviewer_3D9c", "summary": "", "questions": "(1) Could you please provide more details about the implementation of the trainable agents, such as the specific architecture and training settings used?\n(2) Have you considered comparing the trained agents with more baselines, such as other instruction-tuned models or models trained with different methodologies?\n(3) Can you discuss the potential ethical concerns of using trainable agents and how you plan to address them?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for providing us with very constructive comments.  We have addressed your concerns in detail below.\n\n**Q1: Could you please provide more details about the implementation of the trainable agents, such as the specific architecture and training settings used?**\n\n**A1:** To ensure clarity and reproducibility, we will add more details of implementations in the revision and open source to complete the experiment pipeline and results. Here is the outline:\n1) Code and prompts for experience reconstruction;\n2) Experience dataset to train the character simulacra;\n3) Code and hyper-parameters for training the agents;\n4) Generation results of different models (including our trained agents and baselines);\n5) Model weights of our trained agents.\n\nTo address your concerns, we utilized LLaMA 7B to initialize the model and train on the experience reconstruction data for our agents, without modifying the LLaMA architecture. For each agent, we trained with AdamW optimizer for 10 epochs, with the peak learning rate being 2e-5, the maximum length being 2048 and the batch size being 64. The rest of the training details can be found in Section 4.2 in the submission. \n\n**Q2: Have you considered comparing the trained agents with more baselines, such as other instruction-tuned models or models trained with different methodologies?**\n\n**A2:** This is a constructive comment. We recognize that a thorough comparison is essential and have chosen well-established instruction-tuning models as baselines.  These models have been trained using various methodologies, including SFT (Alpaca 7B and Vicuna 7B) and RLHF (ChatGPT). We believe these baselines are sufficient to reflect the effectiveness of the proposed framework. We believe these baselines are sufficient to reflect the effectiveness of the proposed framework. However, to further enrich the comparisons with models having different backbones, we will include additional baselines such as LLaMA 2-Chat and Vicuna 13B in the revised version.\n\n**Q3: 3. About more interview questions**\n\n**A3:** Our interview questions consist of more than 100 different questions per character, for nearly 1000 questions in total. We construct these questions with the assistance of ChatGPT. To ensure diverse coverage and encompass all the aspects we wish to evaluate in the agents, we cataloged various topics and prompted ChatGPT to create interview questions based on these subjects. Upon manual review of the interview questions for one character, we discovered that ChatGPT can generate high-quality interview questions. Some questions are easy to answer, such as \"Where do you come from?\" While others demand a profound comprehension of the characters. For instance, one question for Caesar is: \"Have you ever regretted a decision you've made as a leader?\" Through these questions, we can consistently evaluate the agents' performance in terms of personal knowledge, preferences, values, personalities, and the stability of Character Simulation. We will add detailed statistics and visualization on interview topics and questions. Moreover, we will add more interview questions in the revised version.\n\n**Q4: Can you discuss the potential ethical concerns of using trainable agents and how you plan to address them?**\n\n**A4:** We recognize the importance of clarifying the limitations and ethical concerns surrounding the use of trainable agents. We intend to include discussion sections covering the following points:\n\n**Limitations of Using Trainable Agents**\n\n- Modality: Trainable agents rely on language models, allowing them to manipulate only textual content. Consequently, they are limited in their applicability to tasks that require multi-modal inputs or outputs, e.g. video games and voice assistants.\n- Cost: Since the agents are character-specific, training and deploying a large number of agents to simulate different characters can be both time-consuming and resource-intensive.\n- Editability: Trainable agents may encounter difficulty in adapting to learning new knowledge and skills without further training. \n- Control:  It would be challenging to predict the action agents will take during the interactions, especially in scenarios demanding controlled generated outcomes (e.g., scripted plotlines in stories).\n- Accuracy: Trainable agents might lack the capability to fully emulate the behavior of a given character, and accurately identifying divergent actions can be non-trivial.\n\n**Ethical Concerns of Using Trainable Agents**\n\n- Privacy and Exploitation: Agents could be trained on private or personally identifiable data to simulate specific individuals. For instance, agents might function as user simulators for personalized recommendations. The use of agents to simulate humans raises potential risks. Just as techniques generating fake videos, photos, or voices have ethical implications, trainable agents might generate fabricated texts attributed to targeted individuals. Consequently, constraints on the data usage of trainable agents and rigorous censorship are essential. Moreover, the Data providers should be informed about and give consent for the use of their personal data. In this paper, we utilize publicly available data from Wikipedia to construct the training data and focus on simulating historical and fictional characters to avoid this concern.  Additionally, these trainable agents are strictly designated for research purposes.\n- Harmful Content: There is a risk that agents might be trained to produce harmful or toxic texts using toxic experiential data. To address this, developing tools for toxicity detection is imperative. Moreover, careful alignment of pre-trained large language models is necessary to mitigate and identify the generation of toxic data. By using the safely aligned LLM (gpt-3.5-turbo) for experience reconstruction and the backbone model that is pre-trained on filtered data (LLaMA), we mitigate the toxicity in the trainable agents.\n- Misunderstanding: When employing agents to simulate characters from diverse cultures or historical periods, cultural discrepancies may arise. Trainable agents might misinterpret local culture or ethical norms, leading to subpar performance or even offending local users. Thus, constructing high-quality experiential training and evaluation data is critical. We utilize \"protective experience\" to eliminate unrelated knowledge in agents. By rejecting queries that are beyond the character's knowledge, misinterpretation can be mitigated.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "(1) The paper introduces a novel approach for training trainable agents to simulate specific individuals, which is a step closer to character simulacra.\n(2) The framework proposed by the authors, including experience reconstruction, experience upload, and protective experiences, is well-structured and practical.\n(3) The evaluation process, including interviews and AI-based judging, provides comprehensive insights into the performance of the trained agents.", "reasons_to_reject": "(1) The paper lacks implementation details, such as the specific architecture and training settings used for the trainable agents.\n(2) The evaluation could benefit from comparisons with more baselines and a larger number of interview questions.\n(3) The paper could provide more in-depth discussions on the limitations and potential ethical concerns of using trainable agents.", "questions_to_authors": "(1) Could you please provide more details about the implementation of the trainable agents, such as the specific architecture and training settings used?\n(2) Have you considered comparing the trained agents with more baselines, such as other instruction-tuned models or models trained with different methodologies?\n(3) Can you discuss the potential ethical concerns of using trainable agents and how you plan to address them?", "paper_topic_and_main_contributions": "The paper presents a novel approach for training trainable agents to simulate specific individuals by providing them with profiles and experiences. The authors propose a framework that includes experience reconstruction, experience upload, and protective experiences. They evaluate the trained agents through interviews and compare them with baseline models. The results show that the trainable agents outperform the baselines in terms of personality, memorization, hallucination, and stability. The paper provides valuable insights into building better character simulacra.\n", "excitement": 2, "reproducibility": 2, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "pXpYHwQuz7", "reviewer": "Reviewer_fhYt", "summary": "", "questions": "See above", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank you for your constructive feedback and kind evaluation. We have made a substantial effort in responding to your questions to enhance clarity.\n\n**Q1: Lack of important details.**\n\n**A1:** To ensure clarity and reproducibility, we will add more details of implementations in the revision and open source to complete the experiment pipeline and results. Here is the outline:\n\n1) Code and prompts for experience reconstruction;\n2) Experience dataset to train the character simulacra;\n3) Code and hyper-parameters for training the agents;\n4) Generation results of different models (including our trained agents and baselines);\n5) Model weights of our trained agents.\n\nTo address your concerns, we are using OpenAI's gpt-3.5-turbo with temperature=0.7 for the whole experience reconstruction part, including scene extraction, experience generation and protective experience construction. The prompts used for experience reconstruction can be found in the Appendix. The training details can be found in Section 4.2 in the submission. \n\n**Q2: Does the selection of models for experience reconstruction important?**\n\n**A2:** Thank you for raising this great question. It is straightforward that the more powerful of the LLM is, the better the quality of data it can generate. However, we believe that the capability of LLMs is not the primary bottleneck when it comes to constructing the training corpus for character simulacra. Based on comparisons with models distilled from more data and more powerful LLMs, such as Alpaca 7B (which obtains 52K training data from ChatGPT) and Vicuna 7B (trained on ShareGPT with high-quality responses generated by ChatGPT and GPT-4), the proposed experience reconstruction framework enables our trained agents to achieve comparable performance with very little training data.\n\n**Q3: Does the generated experience faithful and without hallucination?**\n\n**A3:** This is a great question. During the experience reconstruction of historical figures, some scenes or utterances can be hallucinated by large language models. However, our primary focus is not on identifying these hallucinations unless they impact the quality of character simulation, such as inaccuracies in personalities, values, and character memories. We emphasize that Character Hallucinations are the main focus of our work. Distinct from conventional hallucinations in large language models, character hallucinations occur when agents deviate from the character and exhibit unrelated knowledge or abilities of the simulated individual. Exploring this phenomenon further is a direction we intend to pursue in the future.\n\n**Q4: How many scenes are there in the protective experience for training?**\n\n**A4:** We find it can be very effective to add just a few protective experiences in our experiments. The protective experience consists of a 10%-20% proportion of the training data. We plan to include more statistics about the dataset in the revision and open-source the complete experiment pipeline and results.\n\n**Q5: For evaluation, how are the questions/topics selected?**\n\n**A5:** Our interview questions consist of more than 100 different questions per character, for nearly 1000 questions in total. We construct these questions with the assistance of ChatGPT. To make the questions diverse and cover all the aspects that we would like to evaluate on the agents, we enumerated various topics and prompted ChatGPT to write interview questions based on these topics. We manually examined interview questions of one character and found that ChatGPT can generate high-quality interview questions. Some questions are easy to answer like \"Where are you come from?\", while some questions require a deep understanding of the characters, e.g. one question for Caesar: \"Have you ever regretted a decision you've made as a leader?\". By interviewing with these questions, we are able to reliably evaluate the agents' performance on the personal knowledge, preferences, values, personalities and stability of Character Simulation. To further enrich the clarity, we will add detailed statistics and visualization on interview topics and questions in the revised version.\n\n**Q6: Lack of ablation study to show the superiority of the proposed experience construction method. I think adding a comparison between models fine-tuned on different kinds of character data helps.**\n\n**A6:** We agree that it would be helpful to compare our method with models directly fine-tuned on character data. However, we did not add this comparison in the first place because we do not believe it will work. We collect Wikipedia pages as the profiles for experience reconstruction. Without our method, these Wikipedia pages are the only data that is related to the characters. We believe directly fine-tuning language models on these documents leads to poor performance, because \n1) The amount of training data is limited;\n2) There is only a brief introduction about the characters, in which many details are omitted; \n3) The text format is not consistent with the interaction style of agents. \n\nRegardless of these complications, we will add this comparison in the revision to show the effectiveness of our method.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. This paper proposes an experience reconstruction method to extend a character's profile to many detailed scenes that mimic interactions between the target character and other characters. The experience can provide more information for character simulation. This method may inspire future work on character data construction.\n2. This paper proposes constructing and using protective experience to reduce hallucination during role-playing. Trained on protective experience, the model can pretend to be ignorant of the knowledge that the character does not know. The character hallucination problem is important, and this work gives some insight.", "reasons_to_reject": "1. Lack of important details. It's unclear which LLM did the authors use for scene extraction and experience generation (both character experience and protective experience). Does the selection of this model important? Does the generated experience faithful and without hallucination? How many scenes are there in the protective experience for training? For evaluation, how are the questions/topics selected?\n2. Lack of ablation study to show the superiority of the proposed experience construction method. I think adding a comparison between models fine-tuned on different kinds of character data helps.", "questions_to_authors": "See above", "paper_topic_and_main_contributions": "This paper investigates how to simulate a specific person with large language models (LLMs). Different from previous works that prompt existing LLM, the authors construct the experience of that person and then fine-tune an LLM on the experience dataset. The experience consists of (1) character experience generated by another LLM based on the profile, and (2) protective experience that shows the person has no idea about unrelated knowledge. Evaluated by GPT 3.5, the trained model performs better than Alpaca, Vicuna, and is comparable with ChatGPT, in several aspects such as memorizing the character profile and reducing hallucination.", "excitement": 4, "reproducibility": 2, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: Overall, the paper addresses an intriguing subject of how characters can be simulated using LMs. The authors devise a novel methodology and playground for making progress on this that allows them to train agents that they then evaluate for their memories/experiences.

The reviewers jointly all recognize the novelty, thoroughness and insightfulness of many aspects of the work. However, they do also highlight that aspects of rigor (e.g. baselines/ablations, human evals, many important implementation details) could be strengthened. Finally, for this work in particular, I do think there are several ethical concerns with reviewers disagreeing on the extent to which authors address this. If accepted, I would like to see the authors strengthen this aspect based on my own brief reading of the paper. I also encourage the authors to strengthen the implementation details, as I do find the author rebuttals to not adequately address these matters, though the authors do make extensive promises in this vein.

With all of this said, I believe the paper breaks new ground and some of its clear limitations can be addressed as this line of work grows. If the questions of rigor and ethical concern can be addressed, this paper could play a formative role in imaging new possibilities using language models.
Decision: Accept-Main