  Large language models (LLMs) are capable of processing lengthy dialogue
histories during prolonged interaction with users without additional memory
modules; however, their responses tend to overlook or incorrectly recall
information from the past. In this paper, we revisit memory-augmented response
generation in the era of LLMs. While prior work focuses on getting rid of
outdated memories, we argue that such memories can provide contextual cues that
help dialogue systems understand the development of past events and, therefore,
benefit response generation. We present Theanine, a framework that augments
LLMs' response generation with memory timelines -- series of memories that
demonstrate the development and causality of relevant past events. Along with
Theanine, we introduce TeaFarm, a counterfactual-driven question-answering
pipeline addressing the limitation of G-Eval in long-term conversations.
Supplementary videos of our methods and the TeaBag dataset for TeaFarm
evaluation are in https://theanine-693b0.web.app/.
