Commonsense-augmented Memory Construction and Management in Long-term Conversations via Context-aware Persona Refinement (2024)

Abstract: Memorizing and utilizing speakers’ personas is a common practice for response generation in long-term conversations. Yet, human-authored datasets often provide uninformative persona sentences that hinder response quality. This paper presents a novel framework that leverages commonsense-based persona expansion to address such issues in long-term conversation.While prior work focuses on not producing personas that contradict others, we focus on transforming contradictory personas into sentences that contain rich speaker information, by refining them based on their contextual backgrounds with designed strategies. As the pioneer of persona expansion in multi-session settings, our framework facilitates better response generation via human-like persona refinement. The supplementary video of our work is available at https://caffeine-15bbf.web.app/.

## introduction memorizing participants' personal information and conversing accordingly is important for dialogue systems to maintain long-term intimacy with users @xcite . for that, studies have proposed datasets of long-term conversations, which require dialogue systems to memorize and utilize speakers' personas from past dialogue sessions to generate proper responses @xcite . regardless, human-authored personas can be generic and over-simplified, limiting the generation of diverse and engaging responses. intuitively, this can be addressed by expanding existing personas with commonsense expansion @xcite . however, such a naive remedy can raise contradiction between personas (e.g., "i am lazy" and "i clean my room every day"), especially as sessions accumulate (figure 7 ), hindering consistent response generation. while we can simply get rid of contradictory personas utilizing external modules such as models for natural language inference (nli), it yields sub-optimal results (section 3.2). also, avoiding contradictory personas @xcite does not align with human personality traits. since human personality is context-dependent @xcite , we naturally exhibit different personalities and behaviors in different contexts, allowing personas with contradictory interpretations to coexist as one's personas, as shown in figure 1 . motivated by these, in this paper, we tackle such bottleneck of persona expansion in longterm conversations. specifically, we focus on transforming contradictory personas into sentences that contain richer speaker information. to this end, we present caffeine, a context-aware refinement framework for contradictory personas in long-term conversations. caffeine leverages large language models (llms) to iteratively refine the contradictory personas within/across the session(s) based on their contextual background with designed strategies. our contributions are two-fold: (i) to the best of our knowledge, we are the first to explore commonsense-based persona expansion in multi-session settings; (ii) caffeine enables better response generation in long-term conversations in both automatic and human evaluations. also, it refines contradictory personas in a human-like manner, eliciting persona sentences that are superior in remove processed nodes refinement graph g refinement long-term memory next iteration original persona from the current session expanded persona persona from past sessions contextual cues nli 0.97 0.87 0.82 0.93 0.82 0.95 0.81 0.80 commonsense expansion ... ... ... ... i'm addicted to coffee i don't like coffee p1 d1 d2 p2 speaking of which, ? do you like coffee ... a lot of coffee, i thought you despised them? can't live without coffee now. when i am ... caffeine helps me stay focus not really. it's just . too bitter for my taste large language model various criteria while being cost-and time-efficient. ## approach long-term conversations involve multiple dialogue sessions. at the end of each session, we perform: ## conclusion this work pioneers commonsense-based persona expansion in multi-session settings and presents a context-aware refinement framework that leverages contradictory personas to elicit a memory with richer speaker details. caffeine improves response generation in long-term conversations and demonstrates human-like refinement of contradictory personas while being cost-and time-efficient.