title: REALTALK: A 21-Day Real-World Dataset for Long-Term Conversation
summary: Long-term, open-domain dialogue capabilities are essential for chatbots
  aiming to recall past interactions and demonstrate emotional intelligence (EI).
  Yet, most existing research relies on synthetic, LLM-generated data, leaving
  open questions about real-world conversational patterns. To address this gap,
  we introduce REALTALK, a 21-day corpus of authentic messaging app dialogues,
  providing a direct benchmark against genuine human interactions.
    We first conduct a dataset analysis, focusing on EI attributes and persona
  consistency to understand the unique challenges posed by real-world dialogues.
  By comparing with LLM-generated conversations, we highlight key differences,
  including diverse emotional expressions and variations in persona stability
  that synthetic dialogues often fail to capture.
    Building on these insights, we introduce two benchmark tasks: (1) persona
  simulation where a model continues a conversation on behalf of a specific user
  given prior dialogue context; and (2) memory probing where a model answers
  targeted questions requiring long-term memory of past interactions.
    Our findings reveal that models struggle to simulate a user solely from
  dialogue history, while fine-tuning on specific user chats improves persona
  emulation. Additionally, existing models face significant challenges in
  recalling and leveraging long-term context within real-world conversations.
paper_authors._id: 67b7975d10a9714460c03882
paper_authors.name: Dong-Ho Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-21T09:59:30.731Z
paper_authors._id: 6142ec5a7215c6d505bafd4e
paper_authors.avatarUrl: /avatars/ae0387b672435c5a4cf16ff6764ce597.svg
paper_authors.fullname: Dong-Ho Lee
paper_authors.type: user
paper_authors.user: danny911kr
paper_authors._id: 67b7975d10a9714460c03883
paper_authors.name: Adyasha Maharana
paper_authors._id: 67b7975d10a9714460c03884
paper_authors.name: Jay Pujara
paper_authors._id: 67b7975d10a9714460c03885
paper_authors.name: Xiang Ren
paper_authors._id: 67b7975d10a9714460c03886
paper_authors.name: Francesco Barbieri
paper_title: REALTALK: A 21-Day Real-World Dataset for Long-Term Conversation
paper_summary: Long-term, open-domain dialogue capabilities are essential for chatbots
  aiming to recall past interactions and demonstrate emotional intelligence (EI).
  Yet, most existing research relies on synthetic, LLM-generated data, leaving
  open questions about real-world conversational patterns. To address this gap,
  we introduce REALTALK, a 21-day corpus of authentic messaging app dialogues,
  providing a direct benchmark against genuine human interactions.
    We first conduct a dataset analysis, focusing on EI attributes and persona
  consistency to understand the unique challenges posed by real-world dialogues.
  By comparing with LLM-generated conversations, we highlight key differences,
  including diverse emotional expressions and variations in persona stability
  that synthetic dialogues often fail to capture.
    Building on these insights, we introduce two benchmark tasks: (1) persona
  simulation where a model continues a conversation on behalf of a specific user
  given prior dialogue context; and (2) memory probing where a model answers
  targeted questions requiring long-term memory of past interactions.
    Our findings reveal that models struggle to simulate a user solely from
  dialogue history, while fine-tuning on specific user chats improves persona
  emulation. Additionally, existing models face significant challenges in
  recalling and leveraging long-term context within real-world conversations.