REALTALK: A 21-Day Real-World Dataset for Long-Term Conversation
Authors: name: Dong-Ho Lee, name: Adyasha Maharana, name: Jay Pujara, name: Xiang Ren, name: Francesco Barbieri

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.