Faithful Persona-based Conversational Dataset Generation with Large Language Models (2024)

Abstract: High-quality conversational datasets are essential for developing AI models that can communicate with users. One way to foster deeper interactions between a chatbot and its user is through personas, aspects of the user’s character that provide insights into their personality, motivations, and behaviors. Training Natural Language Processing (NLP) models on a diverse and comprehensive persona-based dataset can lead to conversational models that create a deeper connection with the user, and maintain their engagement. In this paper, we leverage the power of Large Language Models (LLMs) to create a large, high-quality conversational dataset from a seed dataset. We propose a Generator-Critic architecture framework to expand the initial dataset, while improving the quality of its conversations. The Generator is an LLM prompted to output conversations. The Critic consists of a mixture of expert LLMs that control the quality of the generated conversations. These experts select the best generated conversations, which we then use to improve the Generator. We release Synthetic-Persona-Chat1, consisting of 20k conversations seeded from Persona-Chat (Zhang et al., 2018). We evaluate the quality of Synthetic-Persona-Chat and our generation framework on different dimensions through extensive experiments, and observe that the losing rate of Synthetic-Persona-Chat against Persona-Chat during an AI detection test decreases from 17.2% to 8.8% over three iterations.

heading: 1 Introduction
text: Every person is a story. Systems that interact with people must understand their underlying stories to effectively engage with them. Unfortunately, many existing datasets used for training conversational
˚Work done during an internship at Google Inc., Mountain View, USA
1Dataset will be publicly available on Github
agents do not sufficiently model their users. Personas - abstract user representations that express the “story” of a person based on their background and preferences - have been widely used for humancentered design in a variety of domains, including marketing, system design, and healthcare (Pruitt and Grudin, 2003b). Prior persona-based conversational datasets, like Persona-Chat (PC) (Zhang et al., 2018), suffer from several limitations, such as small size, static dialogues that cannot easily be updated with new topics, irrelevant utterances, and contradictory persona attributes (Wu et al., 2019). In this paper, we propose a novel framework for generating large, dynamic, persona-based conversational datasets that capture the breadth and depth of human experience.
Personas (Pruitt and Grudin, 2003a; Cooper and Saffo, 1999) have been widely used in a variety of domains and applications, including creating narratives for patients and sharing educational messages in healthcare (Massey et al., 2021), targeting users in marketing (van Pinxteren et al., 2020; Fuglerud et al., 2020), and communicating with workers in management (Claus, 2019). Conversational agents use personas to generate more interesting and engaging conversations with their users (Zhou et al., 2020; Shum et al., 2019).
Creating persona-based datasets is difficult: the process is labor-intensive, the outputs must be updated to reflect current events and new concepts, and there are often quality concerns. Existing persona-based datasets have resulted from laborintensive data collection processes (Zhang et al., 2018; Zhong et al., 2020) involving humans to create or validate personas, create fictional personabased conversations, and ensure the conversations are coherent. Moreover, even after these datasets are created, it is difficult to update them with the latest topics (Lee et al., 2022), such as current events, new concepts, products, or social trends (Lazaridou et al., 2021). Finally, existing persona-based
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datasets do not guarantee faithfulness, a criterion we introduce to describe the alignment between participants’ utterances and their personas.
In this paper, we introduce a new framework for generating large, customized persona-based conversational datasets that uses unsupervised LLMs to reduce human labor, introduces methods to generate, expand, and update personas automatically, and enforces a set of quality criteria including faithfulness to ensure dialogues are human-like. Our persona-based conversational dataset generation framework consists of a three-level pipeline:
1. User Generation
2. User Pairing
3. Conversation Generation
The user generation step takes a set of seed personas, and augments it to create plausible user profiles. The user pairing step matches users to participate in conversations. The conversation generation produces plausible conversations between the selected user pairs. The conversation generation component uses a method similar to self-feedback (Madaan et al., 2023) to iteratively improve the quality of generated samples.
We used the proposed framework to create Synthetic-Persona-Chat (SPC), a conversational dataset with 5k user personas, and 20k faithful dialogues. The framework we defined to create this dataset can be reused to define specialized personas, such as user music profiles, etc. to create application-specific datasets.
Our contributions are:
• We propose an unsupervised approach to generate, and extend specialized personas using LLMs.
• We introduce and evaluate a framework based on LLMs to evolve a dataset while imposing different objectives on it.
• We release Synthetic-Persona-Chat, a highquality, faithful, persona-based conversational dataset useful for several conversational tasks, such as training persona inference models.

heading: 2 Definitions
text: We define the faithful persona-based dialogue generation task. We begin by defining the personabased dialogue generation task. We then formally
define the faithfulness criteria as a desired quality for the generated dialogues. Throughout this section, we use π to refer to persona attributes (individual sentences which, together, form the user persona), U to refer to user profiles, and D to refer to conversations (dialogues).
Persona Attributes We define a user persona attribute as a sentence describing this user. "I like ice cream", "I have two brothers" and "My native language is Tamazight" are all examples of persona attributes. Let Ω be the universal set of persona attributes. Ω contains all natural language descriptions of all tangible features of any person, which is unbounded.
Persona Categories To help organize the vast space of personas, we adopt the approach of Lee et al. (2022) who introduced persona categories. Persona categories are groups of persona attributes that describe the same semantic feature of the user. In our work, we associate each persona category with a corresponding query that can be answered with all persona attributes in that category. For example, job and family situation are persona categories, and corresponding queries might be “What is your occupation?”, and “Do you have a family?”.
Persona Attribute Structure Persona attributes can overlap. For instance, the attribute "I introduced my kids to scuba diving at a young age" overlaps with the attribute "My eldest son goes to elementary school", since both include the "parenthood" feature of the user. Moreover, some persona attributes form a hierarchy, and some persona attributes are specific cases of other attributes.
User Profile We define a user profile as a set of persona attributes that can be used to describe a user. For a realistic user, the persona attributes describing a user profile should not contradict each other, and be consistent. An arbitrary persona attribute set U Ă Ω is a consistent set of persona attribute if, and only if: @π1 P U, EΠ2 Ă U : pΠ2 ‰ Hq ^ pΠ2 Ñ π1q
Persona-based Conversation A persona-based conversation D contains utterances such that at least one persona attribute from each user profile can be inferred from it. For example, the persona attribute "I am a parent" can be inferred from the utterance "I just dropped off my son at school". A persona-based conversation model is a generative model that takes a pair of user profiles (U1, U2) as input, and returns a persona-based dialogue D between these two users.
Faithfulness One crucial quality for a personabased conversation is that it should align with the user profile. Inspired by (Daheim et al., 2023) which introduces dialogue system faithfulness to the knowledge contained in relevant documents, we specify the criterion of faithfulness to characterize the alignment between the utterances of a user in a persona-based conversation and their profile. The faithfulness criterion enforces the constraint that the utterances of a user should not decrease the likelihood of their persona. This criterion assumes the existence of both a prior probability of persona attributes, and an inference model for determining the probability of persona attributes conditioned on utterances. LetM be such an inference model, (U1, U2) a pair of user profiles, and D a persona-based conversation between them. To be a faithful conversation based on M , D should not contain any contradicting evidence to the persona attributes of the speakers: passing the conversation D as input to the inference model M should not reduce the inference probability of persona attributes in either of the user profiles U1 or U2. In other words, the probability of any persona attribute in the user profiles based on conversation D should not be less than the probability of that persona attribute without any assumptions. Formally, we call a conversation D faithful with respect to the user profiles U1 and U2, and inference model M if the following condition holds: @π P U1 Y U2 : PM pπ|Dq ě PM pπq. Where PM pπ|Dq indicates the probability that M infers the persona π given conversation D. We show examples of faithful, and unfaithful conversations in Figure 1.

heading: 3 Method
text: In this section, we introduce our method to generate persona-based conversations. We create such conversations with minimum human input, starting from an initial dataset. Our process consists of three steps, as shown in Figure 2: user generation, user pairing, and conversation generation.
The first component augments a set of seed persona attributes Π0 into an expanded set of persona attributes Πe, from which it creates user profiles. The second component pairs user profiles as interlocutors of a conversation. The third and final component uses an iterative process to generate high-quality conversations among user profile pairs. We detail each of these components below.

heading: 3.1 User Generation
text: The User Generation component is split into two sub-components:
1. Persona Expansion
2. User Profile Construction
We bootstrap seed persona attributes by using various prompts (Brown et al., 2020a) to generate new persona attributes in the Persona Expansion step (Refer to Appendix A.1 for more details on the prompts used). We then create new user profiles by iteratively selecting random user persona attributes from the expanded persona attributes. We employ a Natural Language Inference (NLI) model to ensure the consistency of the constructed user profiles.

heading: 3.1.1 Persona Expansion
text: We propose an unsupervised method to augment a set of seed persona attributes Π0 into a super-set Πe. Unlike previous approaches (Lee et al., 2022), our method is independent of human knowledge or intervention, making it capable of creating specialized personas in new domains. We proceed in two steps: query induction, and persona bootstrapping. In the query induction phase, we identify persona categories in Π0, along with associated queries. We then expand these queries into a setQ that also covers unobserved persona categories. The persona bootstrapping step leverages the category-based query set Q, and the initial persona attribute seed set Π0 to generate new persona attributes. Both of these steps are based on the bootstrapping technique (Yarowsky, 1995), and involve prompting an LLM. We provide a detailed description of these two steps in the following.
Query Induction As described in Section 2, each persona attribute belongs to at least one persona category, and each category is associated with a corresponding query that can be answered with persona attributes in that category. The query induction process initially identifies the queries associated with persona categories in Π0. It then bootstraps queries by feeding them to a prompted LLM to create more queries that are associated with unobserved categories, ultimately creating a query set Q. Including queries associated with unobserved persona categories facilitates the creation of a more diverse set of personas, and increases the scale of augmentation.
The query induction relies on the following assumption:
Assumption Let M be an LLM, and let Γ be the set of all queries associated with all persona categories. If two persona attributes π1 and π2 belong to the same persona category, then there exists a query qM P Γ such that π1 and π2 are M’s output to qM.
The persona attributes "I am a doctor" and "I am a truck driver", for instance, both belong to the "job" category, leading to the query "What is your job?". We use an agglomerative clustering method to identify the persona categories in Π0. Let C be an arbitrary persona cluster in Π0. To generate a query for C, we select a random subset of persona attributes in C, and create a prompt using these samples. We employ this strategy to generate queries for all the clusters identified in Π0, and create a set of queries, which we refer to as Q0. Details on the clustering, query induction, together with examples of clusters, persona attributes, and induced queries are available in Appendix A.1. We come up with queries for new, unobserved persona categories by bootstrapping the queries in Q0: starting from Q “ Q0, we iteratively sample a set of queries from Q, and create a prompt by concatenating them. We then prompt the LLM to generate a new query, and add it to the query set Q, as shown in Figure 3. We generated a total of |Q| “ 188 queries. This set of categoryspecific queries Q is later used to guide the LLM to generate new persona attributes from the specified category. Thus, higher values of |Q| result in greater diversity within the expanded persona attribute set.
Persona Bootstrapping We use the persona attribute seed set Π0 and category-specific queries
Q to generate new persona attributes through a bootstrapping process. We initialize Π to Π0. At every iteration, we randomly select a subset of persona attributes from Π, and create a set of prompts as follows: we first concatenate a set of persona attributes s. For every query q P Q, we then combine the concatenated samples s, and the query q to create a category-specific persona prompt. This prompt guides the LLM to generate a persona attribute for that persona category. The set of prompts obtained from this process is tsq|q P Qu. We only add a new persona attribute to the set if its BERT embeddings (Devlin et al., 2019) are not too close from existing ones, so as to prevent the addition of duplicates.
Each of these prompts is then fed to the LLM to create a new persona attribute, which is subsequently added to the set of persona attributes Π for the next iteration. We continue this iterative process until we have generated a total of 5k persona attributes. Figure 4 illustrates the persona bootstrapping process. Table 7 in the appendix contains the prompt template used in this component.

heading: 3.1.2 User Profile Construction
text: We build user profiles incrementally by sampling persona attributes from Πe, and adding the eligible ones. A persona attribute is eligible if it adheres to the criteria of consistency and non-redundancy. In other words, it should not contradict any attribute already in the user profile, and it should not be inferred by other persona attribute. We assess the
consistency and redundancy of user profiles by leveraging an NLI model, and persona attribute clustering, respectively. The NLI model we employ is based on T5 (Raffel et al., 2019), and has been trained on the TRUE dataset (Honovich et al., 2022).
We create a user profile U by iteratively selecting a random candidate persona attribute π1 P Πe. We use the NLI model to assess whether π1 contradicts any persona attribute in the profile. This is determined by the condition: @π P U : pπ1 Û πq ^ pπ Û π1q, whereÑ is an inference. Additionally, we evaluate the similarity of π1 to the persona attributes in U to prevent the addition of redundant attributes. We add π1 to U if it meets the consistency and non-redundancy criteria. We repeat this process until the user profile contains 5 persona attributes. Please refer to Appendix A.1 for more details on the user profile construction.

heading: 3.2 User Pairing
text: In this component, we identify potential pairs of users for conversations. As the conversations are persona-based, we hypothesize that they will be more engaging if the users’ personas exhibit more commonalities. We assign a similarity score to every pair of user profiles pU1, U2q, indicating their semantic similarity. We leverage BERT to represent the user profiles. The similarity between U1 and U2 is defined as: |tpπ1, π2q|π1 P U1, π2 P U2, Dc : π1, π2 P cu|Where c is a persona attributes cluster. The semantic similarity is quantified by the number of common persona categories in the user profiles. We pair U1 and U2 if their similarity exceeds a threshold of 2.

heading: 3.3 Conversation Generation
text: Our Conversation Generation component is similar to a general-purpose dataset generation framework that generates data samples, and refines them based on a set of predefined criteria, which we refer to as policies (Madaan et al., 2023). The flexibility in the choice of policies for data generation allows us to emphasize different objectives. Once the active policies are selected, this component generates new data samples using a few input samples. The input to our Conversation Generation framework consists of a set of paired user profiles, a few samples of user profiles along with a persona-based conversation between them, and conversation quality metrics as policies. We follow a Generator-Critic architecture, and iteratively create the dataset fol-
lowing the steps shown in Figure 5: Step 1 The Generator outputs candidate conversations between persona pairs using a few initial conversation samples. Step 2 The Critic evaluates the candidate conversations based on the predetermined policies, and selects the best candidate conversations. Step 3 The best candidate conversations are added to the dataset for the next iteration of generation. This iterative process of selecting the top candidates and adding them to the dataset gradually improves the performance of the Generator.
Without any loss of generality, we implement both the Generator and the Critic based on LLMs. Specifically, the Generator prompts an LLM to create candidate conversations, while the Critic prompts an LLM to evaluate the quality of the generated conversations.
We provide more details on the Generator, Critic, and the policies we used.
The Generator outputs conversations for pairs of users pU1, U2q by prompting an LLM (Brown et al., 2020a; Wei et al., 2023). At each iteration, it randomly selects 5 samples from an initial set of conversations, each containing a pair of user profiles and a dialogue among them. It feeds these samples to a template that instructs the LLM to generate a series of candidate conversations for the given user pair. The template, and a sample generated conversation are available in Table 7, and Table 9 in the appendix.
The Critic selects the best generated conversations to fine-tune the Generator. A conversation is deemed high-quality if it complies with the policies of the Critic. Given the multifaceted nature of the conversation evaluations, we use a Mixture of Experts (MoE) approach. Each expert evaluates the conversation based on a specific policy. In this paper, we incorporate three types of experts, each with distinct criteria: general conversation quality, persona faithfulness, and toxicity. Collectively, these experts select the best generated conversations (the single best in our experiments). We describe each type of expert, and the collective
decision-making process below. General Conversation Quality experts assess conversation quality using the Fine-grained Evaluation of Dialog (FED) metrics introduced in (Mehri and Eskénazi, 2020). These experts use verbalized forms of the policies from FED as prompts. For instance, the "conversation depth quality expert" transforms the "depth policy" from FED into a prompt like "Which conversation is a deeper conversation between user 1 and user 2?". Our system instructs the LLM to compare each pair of candidate conversations based on these policies, resulting in pairwise comparisons. The list of policies and their baseline performance are presented in Table 6 in Appendix A.2.
The Faithfulness expert ensures the consistency of the generated conversations with the user profiles. It uses an LLM to identify instances of unfaithful conversations. The faithfulness prompt provides the LLM with explicit instructions, user profiles, and human-curated examples of unfaithful conversations.
The Toxicity expert detects any conversation that exhibits harmful traits, including bias and hate.
The Critic filters unfaithful and toxic conversations out. It then selects the best conversations using a majority vote among the General Conversation Quality experts. The selected instances are added to the dataset for the next iteration of the Generator.

heading: 4 Evaluation
text: We evaluate different aspects of our dataset generation framework, and the resulting dataset - referred to as Synthetic-Persona-Chat - which is created using an instruction fine-tuned LLM with 24 billion parameters (Chung et al., 2022). We compare Synthetic-Persona-Chat (SPC) against the widely used Persona-Chat (PC) dataset across different dimensions. We begin by evaluating the quality of the personas we generate. We then evaluate SPC using both automatic metrics, and human assessment. We analyze other aspects of SPC, such as toxicity and diversity in appendices B.1 and B.1.

heading: 4.1 Evaluation of the Expanded Personas
text: We evaluate our persona expansion module on two seed datasets: Wikipedia, and Persona-Chat. The Wikipedia personas are created by crawling the
1,000 most active contributors2, and extracting user boxes from their pages. We expand both datasets using our framework, and evaluate the expanded persona attribute sets using automatic metrics. Table 1 compares the original persona sets to the expanded ones on a few dimensions. We observe that our persona expansion increases the number of persona attributes in SPC by 119%, while maintaining the original persona categories and expanding them by 71% compared to the persona attributes in PC. Moreover, the lengths of the new generated persona attributes are 107% longer in SPC, indicating that the new personas exhibit greater detail and specificity. We observe a similar trend when applying our persona expansion to the Wikipedia persona set, with a 108% increase in the number of persona attributes, a 140% increase in persona categories, and a 45% growth in persona attribute lengths. This demonstrates the effectiveness of our method in expanding and diversifying persona sets.

heading: 4.2 Next Utterance Prediction
text: A persona-based conversation reflects the speaker’s persona explicitly or implicitly. Therefore, we expect the inclusion of information about speaker personas to enhance the performance of next utterance prediction models in such conversations. In this experiment, we assess the impact of incorporating speaker personas as prior information on both ranking, and generative - Transformer based (Vaswani et al., 2017) - next utterance prediction models. We create a subset of SPC containing conversations among user pairs included in PC for a fair comparison, i.e., for each sample in PC we have a parallel sample in SPC which has the same user pairs but different conversation between them. To create next utterance candidates, we follow PC strategy: for each utterance in a conversation in SPC, we select 19 random utterances from other conversations in the dataset. The number of train, validation and test samples in both cases are 8887, 995, 959.
2https://en.wikipedia.org/wiki/Wikipedia: List_of_Wikipedians_by_number_of_edits
We observe (Table 2) that the performance of ranking models increases when personas are given to the models as input for both datasets. Specifically, the Transformer (Ranker) model, known for its ability to capture conversational complexity, exhibits higher performance in SPC when evaluated on the SPC test set compared to the PC test set. However, it demonstrates relatively weaker performance when trained on the PC. This implies that SPC contains more intricate and coherent conversations.
The Transformer (Ranker) trained on SPC achieves a hit@1 of 64.24 on SPC test, 350% higher than PC (14.24). This suggests that the Transformer model can more accurately predict the next utterance in SPC, pointing to a greater coherency in conversations.
The performance of the Information Retrieval (IR) Baseline model is slightly higher for SPC: it rises by 31% when conditioned on user personas, which is lower than 97% improvement in PC. A key contributing factor for the performance improvement of the retrieval-based model (IR Baseline) on PC given the personas, is the participants’ tendency to copy persona words in the conversations, whereas in SPC the personas are more implicitly reflected in the conversations. The implicit reflection of personas in SPC, makes the task more challenging for word based retrieval models, necessitating reasoning that goes beyond word level. However, when the model is trained on SPC and tested on PC, the improvement is as high as when the model is trained on PC, i.e. 104% compared to 97%.
The performance of generative models is low for this task since these models are not trained with the ranking objective. However, the performance difference while the models are conditioned on personas is lower for the model trained on SPC, with a 20% drop for the model trained on PC against 3% drop in the model trained on SPC. The increase in perplexity is 9% in SPC compared to 41% in PC. The lower rate of perplexity increase and perfor-
mance drop of the model given user personas as input highlights the higher alignment of conversations with personas in SPC.
We also evaluate the performance of the next utterance prediction models when given no user, one user, and both user personas. The results suggest a higher degree of bidirectionality in SPC. We refer the reader to the Appendix B.1 for more details.

heading: 4.3 Human Evaluation
text: We compare the quality of the conversations generated by our framework against those in PersonaChat. We randomly select 200 conversations from PC, together with their corresponding user pairs, and use our method to generate conversations among the same users. We start by following (Gehrmann et al., 2019) in running a human experiment to try and detect AI-generated content. We conduct an AI detection test where we present pairs of conversations to humans, and ask them to identify the synthetically generated one. This test is carried out on the generated conversations at the end of each iteration of creating SPC. We repeat the test for conversations generated for new persona pairs, which we refer to as iteration 3˚, i.e. we pair each of these conversations with a random conversation from PC. For a robust evaluation, every pair of conversations is annotated by 3 human evaluators, and the majority vote is used as the final annotation. Details of this test are available in Appendix B.2. The results of this experiment can be found in Table 3. We observe that the losing rate of SPC is reduced by 48% from SPC Iter 1 to SPC Iter 3, and dropped below the rate of 10%. Interestingly, 91% of the conversations in SPC, which are synthetically generated, are judged as human-like as the conversations generated by humans. Moreover, conversations generated for new personas (Iteration 3˚) are deemed artificial in only 8.04% of cases, showing that SPC is more realistic than PC. We also observe that in Iter 2, from 200 conversations, 79 were different from the
conversations in Iter 1. And in Iter 3, 57 conversations were changed compared to Iter 2. These observations suggest a decreasing rate of updates with subsequent iterations, aligning with our expectations that improvements will reach human-level conversation quality.
We also evaluate the faithfulness of the generated conversations. For each conversation, we provide annotators with a faithfulness annotation task including the speakers’ persona attributes and distractor persona attribute options as shown in Figure 8. We evaluate faithfulness during 3 iterations of conversation generation for the selected 200 user pairs, and the annotators evaluate the generated conversations for each pair in every iteration. The results show that, while improving the Turing test results, faithfulness of conversations are consistently higher than 75% with at most 3% variation in between iterations, indicating high faithfulness in all iterations.
Finally, we assess the impact of LLM size on the quality of the generated dataset within our framework. We create a variant of SPC using an LLM with 540 billion parameters (LLM2). Table 3 presents human evaluations comparing the smaller LLM in multiple iterations to a single-iteration approach with LLM2. The larger model exhibits a 5% advantage in the Turing test over the first iteration of dataset generation over the smaller model. After two iterations, however, the multi-iteration approach outperforms the first iteration of the bigger model, showing our framework’s capacity for costeffective, high-quality conversation generation.

heading: 5 Related Work
text: Large Language Models (LLMs) have been used for data augmentation (Shin et al., 2021), generation (Kim et al., 2023; Dong et al., 2023; Kim et al., 2022), and evaluation (Zhang et al., 2019; Liu et al., 2023). One of the earliest works in this area (Anaby-Tavor et al., 2019) used LLMs to create a large text dataset from a small, labeled one. This idea was followed by (Wang et al., 2021;
Schick and Schütze, 2021) which leveraged LLMs to create datasets without any human data. (Kumar et al., 2020) evaluated the performance of different LLMs on the data augmentation task. Several conversational dataset generation methods focused on the structure of the conversational data (Dai et al., 2022; Leszczynski et al., 2023; Abbasiantaeb et al., 2023). (Mehri et al., 2022) illustrated how LLMs can effectively generate synthetic training data for task-oriented dialogue models.
Persona-based conversations have been a popular research topic in NLP (Liu et al., 2022). One of the earliest works in this area is Persona-Chat, by (Zhang et al., 2018), which proposed the PersonaChat dataset and evaluation metrics that have become a benchmark for persona-based conversation generation (Mazaré et al., 2018). Many subsequent works have used this dataset to train and evaluate (Mohapatra et al., 2021) their models, including DialoGPT (Zhang et al., 2020), BlenderBot (Shuster et al., 2022), and PersonaChatGen (Lee et al., 2022). PersonaChatGen automated the process of creating persona based conversations of PersonaChat using LLMs. A challenge in generating synthetic datasets is to ensure the quality of the conversation including data faithfulness, fidelity, diversity, and consistency (Li et al., 2016; Lee et al., 2023; Veselovsky et al., 2023; Zhuo et al., 2023; Wang et al., 2023a; Mündler et al., 2023). Several works have focused on creating and using high quality training datasets (Welleck et al., 2019), and creating quality filtering components to their conversation dataset generation (Lewkowycz et al., 2022). Evaluation of the resulting conversational datasets is also challenging (Xu et al., 2021). (Wang et al., 2023b) recently introduced the paradigm of interactive evaluation of conversations with LLMs.

heading: 6 Conclusion and Future Work
text: We developed a novel framework for generating high-quality persona-based conversations using LLMs, resulting in the creation of SyntheticPersona-Chat, comprising 20k conversations. We hope this dataset will support future endeavors in developing persona-aware conversational agents, including the generation of domain-specific multisession conversations for specialized, task-oriented interactions. While we focused on a persona-based dataset generation task, our Generator-Critic approach can be generalized to other use cases, such as generating other specialized datasets, etc.

heading: Limitations
text: In this paper, we define an iterative process over LLMs to generate a dataset. Our method requires computational resources, and access to an LLM. The quality of the dataset is bounded by the LLM, since the quality critics are also using the same LLM, and we leave the iterative improvement of our critics as future work. The main limitation of this data generation framework is the inability to generate realistic conversations that do not have high quality, since we assume that both parties are fluent, that the conversation flow is perfectly consistent, and there is no unexpected event (e.g. an interruption by another person, connection loss, etc.) in the middle of the conversation. Another limitation of our method is the difficulty of incorporating less tangible persona traits, such as a sense of humor, or user attributes that require multiple conversation sessions to be reflected.

heading: Ethics Statement
text: The approach of generating datasets based on some desired objective might be used to create harmful datasets, and train malicious models based on them, such as a biased dataset, or a hateful speech one (Hartvigsen et al., 2022). On the other hand, these datasets and models can be used as filters in application tasks.
We used Amazon Mechanical Turk in our human experiments, and followed that platform’s guidelines to protect the rights of human raters. The participation was voluntary, and the raters were informed of their rights at the beginning of the study. The platform implemented security measures to protect them, and prevent the disclosure of any Personal Identifiable Information about them. Furthermore, we offered higher than minimum standard wage compensation to avoid any exploitative practices.
To avoid having any toxic conversation in the final dataset, we also used several tools to remove any potentially toxic conversation. Details about these tools, and example removed samples are available in Appendix B.1.

heading: A Dataset Generation Framework
text: In this section, we provide more details on our synthetic dataset generation framework. We created Synthetic-Persona-Chat using an LLM with 24 billion parameters. We use top-k sampling with k “ 40 for decoding during generation, and set the temperature value to 0.7 in all components. We give more details on user and conversation generation components in the following subsections.

heading: A.1 User Generation
text: In our framework, the user generation component consists of two steps: expanding the persona attribute set, and creating realistic user profiles. In this section we provide details on our framework for these two steps:
Persona Expansion As described in Section 3.1.1, the persona expansion step involves identifying persona categories in the initial persona attribute set Π0, generating queries associated with those categories, and bootstrapping queries to create a query set . In our framework, we employ the Scikit-learn (Pedregosa et al., 2011) implementation of an agglomerative clustering to identify persona categories following this clustering method: we represent each persona using a BERT-based representation. Our clustering approach is bottom-up, starting with each persona attribute as an individual cluster. At each step, we combine two clusters if their similarity exceeds a predetermined threshold of 0.1. The similarity of two clusters is measured using inter-cluster average cosine similarity. The process continues until no pair of clusters is more similar than the threshold. We set the value of the threshold as 0.1 since it lead to more than 100 nonsparse clusters, i.e., clusters that include at least 3 persona attributes and can be used in the query induction prompt. Table 4 presents the cluster similarity threshold values and the resulting cluster details based on them.
After identifying the clusters, we sample 3 instances of persona attributes for each cluster, and prompt the LLM using the template in shown in section 3 to construct an initial query set Q0. We expand the query set Q0 using bootstrapping. At each step, we sample 5 instances from the available queries, and prompt the LLM using the template in Table 7. We repeat this process for 100 steps. Examples of initial persona attributes, induced queries, bootstrapped queries, and bootstrapped persona attributes can be found in Table 5. The prompt tem-
plates used in this component are available in Table 7.
User Profile Generation We illustrate a sample user profile creation process in Figure 6. As shown in the figure, at each iteration, a randomly selected persona attribute is checked for consistency and non-redundancy.
Let π1 be a randomly selected persona attribute in an iteration. For the redundancy criteria, we use the BERT representation of persona attributes. We compute the similarity of the new candidate persona attribute π1 with every persona attribute in the user profile. If it is more than a threshold (0.9 in these experiments) similar to an attribute in the user profile, π1 is deemed as redundant and will not be added to the user profile. We use the cosine similarities of the BERT representations of the persona attributes. The value of the similarity threshold is selected to be compatible with the agglomerative persona clustering algorithm in the persona expansion step, in which two clusters are merged if their inter-distance is less than 0.1, i.e., their inter-cluster similarity is higher than 0.9. Therefore, by setting the threshold of similarity of attributes to be .9, we ensure that the new attribute is added to the user profile if it is from a new cluster compared to the current attributes in the user profile.
For the consistency criteria, we use the NLI model to verify the consistency of this persona attribute with the user profile. For every persona attribute in the current user profile π, we prompt the LLM to create the negated persona attribute π. Then, we query the NLI model to check whether π is inferred by π1 or π1 is inferred by π. If either of these cases is inferred, then the selected persona attribute is not consistent with the user profile, and not added to the profile.

heading: A.2 Conversation Generation
text: LLM-based Critic In our framework, the critic is implemented by prompting an LLM. We included a mixture of experts approach in the critic, where each expert prompts the LLM to assess a
specific policy in the candidate conversations. Our framework includes a set of experts to control the general conversation quality. We evaluate the performance of these experts using a baseline dataset. The baseline dataset for this experiment is FED which consists of 125 human-annotated instances evaluated at the conversation level. We pair the conversations and evaluate the experts based on the number of correctly ranked pairs. As shown in Table 6, we observe that these experts are more than 80% accurate in distinguishing the better conversation within the pairs. The template for the verbalized form of these experts used in our frame-
work can be found in Table 7. We also included a toxicity expert and a persona faithfulness expert in the critic. The prompt templates used in these experts are available in Table 7. The persona faithfulness leverages in-contextlearning capability of LLMs. It includes a few human-curated examples of faithful and unfaithful conversations in the instruction prompt. Refer to Table 8 for examples of faithful and unfaithful conversations used in the instruction prompt.
The faithfulness critic, prompts the LLM both with and without the candidate conversation between two users. It assesses the log probabilities of the output being "Yes" (indicating a contradiction and thus unfaithfulness) or "No" (indicating no contradiction and thus faithfulness). A conversation is deemed unfaithful if there is an increase in the
probability of a contradiction ("Yes") or a decrease in the probability of no contradiction ("No").

heading: B Synthetic-Persona-Chat
text: Synthetic-Persona-Chat is made of 20k conversations, with an average of 11.8 turns per user for each. An example Synthetic-Persona-Chat conversation can be found in Table 9. We compare Synthetic-Persona-Chat to Persona-Chat across different dimensions. We first assess the characteristics of SPC using various automatic evaluators, i.e. evaluators which do not require human effort. We then conduct a human evaluation experiment on a subset of SPC.

heading: B.1 Automatic Evaluation
text: We conduct a comprehensive analysis and evaluation of SPC across different dimensions and compare it against PC. We start by analyzing the toxicity and diversity of SPC using off the shelf tools. Then, we elaborate on the experiments which assess the efficacy of SPC used as the dataset for the next utterance prediction and the profile extraction tasks. Finally, we evaluate the quality of SPC conversations using LLM-based evaluation methods.
Toxicity Analysis We analyze the toxicity of the generated conversations at the final iteration of SPC using an online tool called Perspective3. We reproduce the results of a detailed analysis of toxicity in PC as well as in each iteration of our data generation framework while producing SPC in Table 10. We observe a notable reduction in the frequency of conversations deemed as strongly toxic or profane throughout the iterations of generating SPC. This reduction can be attributed to the built-in toxicity filter of the employed LLM. While PC contains more than 50 samples that are identified as strongly toxic, SPC includes at most three toxic or profane conversations, which is significantly lower (at least 15 times less). Interestingly, the fraction of conversations with medium profanity and toxicity in SPC is 4 times less than the same type of conversations in PC across all iterations. We have removed any conversation that was marked as strongly toxic by this tool in the released dataset. Samples of toxic conversations are provided in Table 11.
Diversity Analysis We use hierarchical topic modeling (Blei et al., 2004) to assess the topic diversity of SPC and compare it to that of PC. For a
3https://perspectiveapi.com/
fair comparison, we only compare conversations in SPC with similar personas in PC. Table 12 displays the number of topics at each level of the topic tree, with the first level indicating the most general topic. We observe similar topic diversity at the first level. In deeper levels, there is a slightly lower diversity in SPC.
Next Utterance Prediction We compare the performance of different models on the next utterance prediction task. As discussed in Section 4.2, these models are expected to exhibit better performance in the next utterance prediction task when user personas are provided as prior information. We evaluate ranking and generative models for response selection to assess this property. We compare models trained on SPC to the same models trained on PC. We use the implementations provided in (Miller et al., 2017) for the following models:
• IR Baseline Given an utterance as a query, the IR baseline finds the most similar utterance in the training corpus using tf-idf. It defines the utterance after the most similar utterance as the candidate response, and then returns the most similar option to that candidate as the output.
• Transformer-Ranker The context of the conversation, as well as the candidate next utterances, are encoded using a BERT-based encoder. The most similar encoded candidate to the conversation context, as measured by a dot-product in their representation space, is selected as the output (Humeau et al., 2020).
• Transformer-Generator This model is a sequence-to-sequence model (Sutskever et al., 2014) which uses transformers as encoders and decoders.
We also evaluate the performance of the next utterance prediction models when given no user, one user, and both user personas. The results of this experiment are available in Table 13. We observe that the highest performance improvement for all models trained on PC is when self-personas are given as input. We do not observe such a pattern in SPC. This indicates a higher degree of bidirectionality in SPC conversations compared to those of PC.
Profile Extraction A potential use-case of the SPC dataset is training a model to predict user personas from a conversation. This is only possible if
the dataset is highly faithful, meaning that any persona attribute inferred from the conversation is in the user profile or compatible with the user profile. In this context, a faithful conversation is expected to have high precision in the profile extraction task, while a conversation that highly reflects user personas is expected to have high recall in this task.
We evaluate the task of user profile extraction for conversations in SPC, and compare the results against those of PC. We frame the task of profile extraction as a ranking task, using the utterances within the conversations as queries. The goal is to rank a set of persona attribute options. For each conversation, we include the speakers’ persona attributes in the available options. Additionally, we select 25 random user persona attributes from other speaker profiles within the dataset to serve as distractors. The input to the profile extraction is utterances from a single user as the speaker, while the output is a list of persona attribute options for a target user, which could be either user 1 or user 2. The results of this experiment are presented in Table 14. We observe that the performance of the profile extraction methods is higher in SPC in 3 of the 4 scenarios. Interestingly, we observe that with both datasets, when the target and the speaker are
different, the performance of profile extraction is greater compared to the cases when the target and speaker users are the same.
LLM-based Quality Evaluation We leverage LLM-based conversation quality evaluators from the literature to compare the quality of SPC and PC. These evaluators rely on the human curated prompt templates for different metrics including consistency, fluency, etc. We used these evaluators with minimum change in the original prompt templates. These evaluators are:
• LLM-Eval (Lin and Chen, 2023) is a multidimensional automatic evaluation designed for conversations. It uses a human-curated prompt which describes evaluation dimensions, serving as a unified evaluation schema. This prompt evaluates the conversation across multiple dimensions (e.g. fluency) in a single model call. We show this unified schema in Table 15.
• GPT-Score (Fu et al., 2023) leverages emergent abilities of LLMs, i.e. zero-shot instructions, to score texts. It contains a prompt template, and for each quality criterion, populates the template with a human description of the criteria along with the valid score range for that criteria. Example prompts are provided in Table 15.
• G-Eval (Liu et al., 2023) introduces a framework that employs LLMs with a chain-ofthought approach to assess the quality of natural language generated outputs. For any evaluation criteria, G-Eval prompts the LLM with the criterion’s description, prompting the
model to generate the necessary evaluation steps. It then uses these steps to prompt the LLM to score given output for that criterion. It considers the probability of getting each permissible score as the output of the prompt, i.e., it considers the probability distribution of scores assigned by the LLM. The reported output is the expected value of the score distribution by the LLM. Table 15 includes an example prompt.
Results of this evaluation are presented in Table 16. We observe that SPC consistently outperforms PC across all the dimensions we evaluate. The superiority of SPC is more prominent when using GPT-Score, for which each evaluated criterion shows an average improvement of at least 23 points.

heading: B.2 Human Evaluation
text: We run a human evaluation of the performance of our method via a crowdsourcing platform. We conduct an AI detection test, and a faithfulness study - both of which we describe in more details in the following subsections - at the end of every iteration of the generation of SPC.
AI Detection Test We randomly select 200 user pairs from PC. For each example, we show the annotators the user pair, together with the corresponding conversations from PC and SPC, and ask them to select the conversation that was synthetically generated. We show an example of this crowd-
sourcing task in Figure 7. The results of the AI detection test are available in Table 17. We report the losing rate of SPC in the AI detection test, and Fleiss’ Kappa to assess the inter-rater agreement. The agreement falls into the fair to moderate agreement bucket.
Faithfulness We present the annotators with a conversation, and a set of options of persona attributes. The annotators are asked to select the user persona attributes they would infer from the conversation. Figure 8 shows a sample of the annotation task in this study. The options include the persona attributes of the speakers in the conversation, and a set of distractor persona attributes. We created distractor persona attributes using different strategies to cover different difficulty levels. For a persona attribute set Π, we create a set Π of distractor persona attributes as:
Negated personas We prompt an LLM to negate persona attributes. For example, the negation of persona attribute "I like vegetables" is "I don’t like vegetables".
Random personas We randomly select persona attributes from user profiles in other conversations in the dataset.
Contradicting personas We prompt an LLM to generate a persona attribute which contradicts the users’ personas.
Each entry of this task includes 8 user persona attributes as options, where 4 of them are the real persona attributes, and the other 4 are distractors. We evaluate the precision of the human annotators, and report it as a proxy to the conversation faithfulness in Table 3.

heading: C Ablation Studies
text: We run several ablation studies to evaluate the importance of individual components in our framework. We begin by analyzing the effect of the persona expansion module. We then review the impact of each expert in the mixture forming our Critic.

heading: C.1 Persona Expansion
text: We assess the importance of the query-based persona expansion module introduced in Section 3.1.1. Similarly to the experiment outlined in Section 4.1, we run the persona expansion on two datasets: Wikipedia and PC. The results of this experiment are presented in Table 18. We designate the persona expansions without the inducted query set (Q) as ‘Wikipedia-0’, and ‘PC-0’, and run the same number of iterations for each (100 iterations). We observe that PC-0 includes 4,477 new persona attributes, 20 percent less than PC. The difference in the number of newly generated persona attributes is more pronounced in the case of Wikipedia, where Wikipedia-0 consists of 4,742 persona attributes, 50 percent less than Wikipedia+. This trend is also observed in the number of persona clusters, with PC-0 and Wikipedia-0 having 6% and 49% less clusters respectively. This pattern suggests the effectiveness of the query-based persona expansion in maintaining the diversity of the persona set. Furthermore, the average persona attribute length in PC-0 is 11.38 tokens, which is 28% less than SPC. This reduction points to less detailed and specific persona attributes. In contrast, the expansion in ‘Wikipedia-0’ exhibits similar average persona attribute lengths compared to ‘Wikipedia+’.

heading: C.2 Conversation Quality
text: We analyze the effect of the experts within our Critic. We remove each expert, and generate a dataset using one iteration of our framework. We compare the resulting datasets against the output of the first iteration of SPC. We use the evaluators introduced in B.1. The results of this experiment are summarized in Table 16. We observe that the exclusion of the experts results in worse performance according to most criteria: 3 out of 4 in LLM-Eval, 4 out of 6 in GPT-Score, and 3 out of 5 in G-Eval.

heading: C.3 Faithfulness
text: We ablate the faithfulness critic, and generate a dataset that we compare against SPC. We compare these datasets both automatically, using human annotators (AI detection test), and using a prompted LLM (LLM-Evaluator). We describe this study in more details below.
AI Detection Test We run a human study to compare a small subset of conversations created without the faithfulness expert against their equivalent created with that expert. This experiment process is similar to 4.3 and it is conducted for 200 conversations. The precision decreases from 78.0% to 66.0% without this critic, highlighting its effectiveness in eliminating conversations with contradictory information about user personas. The recall decreases from 36.0% to 23.0%, demonstrating a higher reflection of personas in the conversations in the presence of the faithfulness expert.
LLM-Evaluator We extend our comparison to the entire dataset using an LLM as an annotator, following (He et al., 2023; Bansal and Sharma, 2023; Chiang and yi Lee, 2023). Table 19 shows the faithfulness of the conversations generated in the first iteration without the faithfulness expert. The templates used in the LLM-based annotators are described in Table 16 in the rows with "LLMFaithfulness" as their evaluator. Note that the annotator-based LLM is created using a different LLM, gpt-3.5-turbo (Brown et al., 2020b; Ouyang et al., 2022), than the LLM used for dataset generation.

heading: C.4 Next Utterance Prediction
text: We follow the experimental setting described in section 4.2, and compare the performance of various next utterance prediction models trained on SPC against the same models trained on datasets created in the absence of certain experts.
When using the IR Baseline as the next utterance prediction method, we observee that its highest performance of 39% hit@1 occurs when the FED critic is absent during dataset creation. This outcome aligns with FED’s emphasis on conversation quality, excluding persona-related aspects. Conversely, the Transformer Ranker, capable of understanding intricate concepts, achieves its peak performance of 13.9% hit@1 when none of the experts are absent. This result supports the inclusion of both FED and the Faithfulness expert in the
model architecture. In generative models, the absence of FED impacts the next utterance prediction model the most, leading to a notable decline in performance (e.g. ´12% hit@1, ´9% BLEU, ´10% ROUGE). This observation underscores the crucial role played by FED in enhancing the generative capabilities of the model.

heading: Future Work
text: The quality of the dataset is bounded by the LLM, since the quality critics are also using the same LLM, and we leave the iterative improvement of our critics as future work. The main limitation of this data generation framework is the inability to generate realistic conversations that do not have high quality, since we assume that both parties are fluent, that the conversation flow is perfectly consistent, and there is no unexpected event (e. g. an interruption by another person, connection loss, etc. ) in the middle of the conversation. Another limitation of our method is the difficulty of incorporating less tangible persona traits, such as a sense of humor, or user attributes that require multiple conversation sessions to be reflected