LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive
  Memory
Authors: name: Di Wu, name: Hongwei Wang, name: Wenhao Yu, name: Yuwei Zhang, name: Kai-Wei Chang, name: Dong Yu

Recent large language model (LLM)-driven chat assistant systems have
integrated memory components to track user-assistant chat histories, enabling
more accurate and personalized responses. However, their long-term memory
capabilities in sustained interactions remain underexplored. This paper
introduces LongMemEval, a comprehensive benchmark designed to evaluate five
core long-term memory abilities of chat assistants: information extraction,
multi-session reasoning, temporal reasoning, knowledge updates, and abstention.
With 500 meticulously curated questions embedded within freely scalable
user-assistant chat histories, LongMemEval presents a significant challenge to
existing long-term memory systems, with commercial chat assistants and
long-context LLMs showing 30% accuracy drop on memorizing information across
sustained interactions. We then present a unified framework that breaks down
the long-term memory design into four design choices across the indexing,
retrieval, and reading stages. Built upon key experimental insights, we propose
several memory designs including session decomposition for optimizing value
granularity, fact-augmented key expansion for enhancing the index structure,
and time-aware query expansion for refining the search scope. Experiment
results show that these optimizations greatly improve both memory recall and
downstream question answering on LongMemEval. Overall, our study provides
valuable resources and guidance for advancing the long-term memory capabilities
of LLM-based chat assistants, paving the way toward more personalized and
reliable conversational AI.