P ersonality C hat: Conversation Distillation for Personalized Dialog Modeling with Facts and Traits (2023)

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

## introduction training deep learning models and architectures to act as decent dialog agents is a data-hungry task. historically, big conversational datasets have been either curated from already existing dialog or sudodialog resources (e.g. reddit @xcite , ubuntu @xcite , opensubtitles @xcite ), or via crowd-sourcing, which is usually the case if specific features are desired (e.g. persona-grounded @xcite , empathetic @xcite ). but these methods are noisy, costly or limited in the scope of feasible grounding features. recently, large language models (llms) like gpt-3 @xcite , chatgpt, gpt-4 and opt @xcite provided a cheaper and more flexible alternative, which has been rapidly adopted to augment existing datasets @xcite @xcite or generating new ones @xcite persona: i like to tan in tanning beds. i love spending money. i am high maintenance. i diet a lot. i love listening to britney spears. personality traits (chatgpt): openness: no clues. conscientiousness: probably low, given the statement i love spending money and that they admit to being high maintenance. extraversion: probably high, as they enjoy tanning and listening to music, both of which are often done in social settings. agreeableness: no clues. neuroticism: probably high, as they state that they diet a lot and may have insecurities about their appearance. in this study, we exploit this flexibility to distill a corpus which is difficult and expensive to curate from human interactions: a conversational dataset conditioned on both personas and the big-5 personality traits. to prompt the llm, we employ persona statements from the popular personachat dataset @xcite and enrich them with relevant labels from the well-known big-5 personality traits @xcite which we also obtain using llms. besides generating the first sizeable dialog corpus curated explicitly with personality traits, this parallel approach allows us to perform a fair comparison between the crowdsourced personachat and its distilled counterpart. our contributions are as follows: 1) we release personalitychat; a distilled personalized dialog dataset grounded on persona and personality traits. ## personalitychat generating a diverse set of conversations from llms is usually done using a varied collection of scenarios or settings which provide the model with background information. for example @xcite employed short narratives based on commonsense knowledge, and @xcite used a list of topics and tasks from the fits dataset @xcite . we base our work on personachat @xcite , the most popular dataset for dialog personalization which was curated by pairing online crowd-source workers and asking them to converse with each other using their assigned personas; a small set of simple personal statements about occupation, interests, hobbies etc. (e.g. the overall 2-step curation pipeline is shown in figure 1 : first chatgpt is employed to predict personality traits for personas from the personachat dataset. then traits are sampled from these labels and used along with the personas to prompt chat-gpt to generate dialogs grounded on both features. next we describe these two steps in detail. ## training with personalitychat in this section we perform experiments to answer the following questions: ## conclusion in this work we exploited the highly customizable generation skills of chatgpt to build a corpus which is difficult and expensive to curate from human interaction; i.e. a sizeable dialog dataset grounded in both personal facts and (big-five) personality traits. starting from the personachat dataset, we enriched the persona statements with personality trait speculations and used them to prompt chatgpt for personal dialog. we explored the qualities of this dataset in two parts: 1) trait modification, which showed that the overall 'attitude' of a dialog agent trained on our dataset can be modified using trait labels, specially for openness, extraversion and agreeableness. 2) comparison with personachat (using only persona facts) made possible by the parallel one-to-one curation scheme, which showed our distilled dataset can be used to train smaller dialog models with better performance, specially in terms of naturalness and coherence. we also faced and discussed some of the potential shortcomings of our approach. most importantly, the 'easy-training' advantage of a distilled dataset comes with a less diverse and more predictable language distribution which can make a dialog agent sound 'boring' on the long run. combining the natural and synthetic datasets can be a solution but (at least in our experiments) the distilled distribution seems to mostly overrule the natural one. in trait modification, we observed interesting results in the way trait labels can affect and steer model's conversational behavior but there is a lot of room for improvement (e.g.