Year: 2026
Type: conference-paper
Abstract: Memory serves as a pivotal component in interactive response generation, supplying essential background information and referential knowledge for dialogues.Conventional interactive algorithms have predominantly treated memory as a merely contextual element, largely neglecting the nuanced cognitive processes involved in individualized memory encoding and retrieval.This conceptual gap has led to the prevailing schema where memory-enhanced dialogue datasets incorporate monolithic, undifferentiated memory content, failing to capture the personalized nature of persoa memory processing.Grounded in the self-reference effect from cognitive psychology, we introduce a Multi-Turn Dialogue Dataset with Personalized Contextual Memory (D 2 PCM), establishing a comprehensive benchmark to facilitate advanced research on personalized memory processing algorithms.
Institution: Tsinghua University
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166798272