We Are What We Repeatedly Do: Inducing and Deploying Habitual Schemas in Persona-Based Responses (2023)

Abstract: Many practical applications of dialogue technology require the generation of responses according to a particular developer-specified persona. While a variety of personas can be elicited from recent large language models, the opaqueness and unpredictability of these models make it desirable to be able to specify personas in an explicit form. In previous work, personas have typically been represented as sets of one-off pieces of self-knowledge that are retrieved by the dialogue system for use in generation. However, in realistic human conversations, personas are often revealed through story-like narratives that involve rich habitual knowledge – knowledge about kinds of events that an agent often participates in (e.g., work activities, hobbies, sporting activities, favorite entertainments, etc.), including typical goals, sub-events, preconditions, and postconditions of those events. We capture such habitual knowledge using an explicit schema representation, and propose an approach to dialogue generation that retrieves relevant schemas to condition a large language model to generate persona-based responses. Furthermore, we demonstrate a method for bootstrapping the creation of such schemas by first generating generic passages from a set of simple facts, and then inducing schemas from the generated passages.

## introduction virtual conversational agents -simulated humans that can engage in conversation with a human user -present a major application of dialogue technology. such systems have been deployed for diverse uses including conversational coaches, chatbots for entertainment, and customer service bots. a critical, yet challenging, problem in designing conversational agents is endowing them with a specific persona, and generating responses that are both natural and consistent with this persona. systems that are able to do this are both found to be more engaging by users @xcite , and increase (1) given an unstructured persona dataset, we first sample "generic passages" from the facts in the persona, and then induce structured event schemas from the sampled stories. (2) we condition an llm to generate dialogue responses that are fluent with previous conversation -yet that make use of the rich knowledge contained in the resulting schemas -by first using a retrieval model to select a relevant schema, and then providing the schema to the llm as in-context knowledge. the level of confidence and trust that users place in the system @xcite . furthermore, in many practical applications beyond chit-chat, there is a complementary need to control the flow of dialogue; for example, ensuring consistency of generated responses with hand-engineered templates may help to improve a dialogue system's topical coherence @xcite . seminal conversational systems such as eliza @xcite operated on the basis of symbolic knowledge, allowing directly for persona development through the manipulation of ex-plicit rules. however, this dependence on explicitly coded knowledge also rendered such systems knowledge-impoverished, and unable to make obvious inferences. decades of ai research, aimed at solving this problem, have culminated in the creation of large language models (llms), and the emergence of in-context learning -i.e., steering the llm towards particular behavior by including knowledge or examples in the natural language prompt @xcite . recent work has found that systems that leverage in-context learning with llms outperform those that fine-tune smaller language models on conversational data @xcite . while a variety of personas can be elicited from the information implicit in the weights of llms, the resulting personas are often unpredictable, opaque to dialogue designers, and prone to hallucination @xcite . therefore, much recent work has focused on representing personas and knowledge explicitly, in a manner that can be leveraged by llms for generation using retrievalin-the-loop methods @xcite . typically, these approaches represent personas using unstructured sets of natural language "facts" about an agent, possibly augmented with additional knowledge from a knowledge base. in casual human-human dialogue, however, personas are often revealed through story-like narratives about experiences rather than one-off facts @xcite . for example, if a speaker mentions something involving sports, the interlocutor might respond by relating their typical experiences playing a sport in the past. these types of narratives, typically taking the form of "generic passages" @xcite , often capture habitual knowledge -knowledge about the kinds of events that an agent participates in, or used to participate in. this knowledge includes the typical steps of a habitual event, as well as the typical goals, preconditions, and postconditions of the event. originating from early research in artificial intelligence, event schemas have been proposed as a structured representation of the rich types of prototypical knowledge associated with generic and habitual events, such as causal and enabling relations, temporal relations, etc. @xcite @xcite . in this paper, we propose a novel approach to dialogue generation that uses a collection of explicit event schemas to augment an agent's persona, and that conditions an llm to generate narrative-like responses consistent with these schemas through in-context prompting foot_0 . furthermore, since it is often desirable for dialogue designers to be able to specify a persona using a small number of simple natural language facts, we propose a method for bootstrapping the creation of schemas from a set of simple facts. this method involves leveraging llms to first generate "generic passages" from the given facts, and then to induce structured habitual schemas from the passages -capturing both explicit steps from the passage and implicit knowledge associated with the event described by the passage. a high-level diagram of our approach is shown in figure 1 . we present evaluation results showing that the generated schemas are generally high quality, and can be used to condition llms to generate responses that are more diverse and engaging, yet also controllable. ## method given a dialogue context @xmath0, u 2 , ..., u n-1 } containing system and user utterances, our goal is to generate a response u n that utilizes knowledge from a relevant event schema s @xmath1, s 2 , ..., s m } -this schema represents knowledge about a habitual activity that is part of the speaker's persona and that is relevant to the previous turn u n-1 . we ensure that the selected schema is relevant using a multi-level information retrieval system to embed both the event schemas (treated as individual documents) and the knowledge contained within each event schema (treated as collections of documents), and to rank the schemas in s based on similarity to the embedding for u n-1 . following @xcite , we employ a prompting-based approach in which a pre-trained llm is used to produce a response utterance, provided a prompt that is dynamically constructed from the dialogue history and the selected schemas. ## experiments we first evaluate our response generation method according to the following desiderata: (1) the generated responses improve diversity of output; since an important advantage of our approach is the reusability of the generated schemas for downstream tasks (e.g., for inferring additional facts from a dialogue agent's experiences), we also conduct an evaluation of the quality of the generated schemas -specifically, whether the facts within the schema correctly represent typical knowledge associated with the event that the schema describes. ## conclusion and future work in this work, we demonstrated that habitual knowledge in the form of explicit event schema representations could be used to condition llms to generate more diverse and engaging dialogue responses. we experimented with two generation settings, one of which furthermore allows for a greater degree of controllability by a dialogue designer who may wish to provide intended utterances for the llm to paraphrase. moreover, to ease the burden of schema design, we proposed a novel method of inducing schemas from a base persona using an llm through sampling "generic passages" about habitual activities. although the inclusion of habitual knowledge 9 one important caveat is that this behavior is not necessarily undesirable; short open-ended questions can often be used in a conversation to demonstrate interest or empathy towards the interlocutor, although in this paper we are focused on the challenge of generating more engaging responses. can be used to produce more engaging responses, it is not sufficient -often, conversations focus around more specific experiences and memories, and the knowledge captured by schemas generated with our approach can be somewhat banal. in future work, we aim to extend our approach to generate schemas that capture atypical aspects of an agent's experience with a particular kind of event, as well as more ordinary memories or knowledge. we also aim to incorporate this response generation mechanism into a broader dialogue management framework that allows for a higher-level decision about whether responding using a habitual schema is appropriate given a particular context.