THEANINE: Revisiting Memory Management in Long-term Conversations with
  Timeline-augmented Response Generation
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outdated memories, we argue that such memories can provide contextual cues that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/.
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Seo Hyun Kim
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2024-06-19T07:39:19.479Z
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Seo Hyun Kim
user
shkim0116
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Kai Tzu-iunn Ong
admin_assigned
2024-06-18T08:17:37.450Z
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kai tzu-iunn ong
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ktio
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Taeyoon Kwon
admin_assigned
2024-06-18T08:38:18.918Z
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Taeyoon Kwon
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Connoriginal
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Namyoung Kim
claimed_verified
2025-09-24T14:00:20.212Z
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Namyoung Kim
user
kimnamssya
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Keummin Ka
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2024-06-18T08:38:39.991Z
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Keummin Ka
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KEUMMin
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SeongHyeon Bae
6671145dab84abdbee7765a8
Yohan Jo
6671145dab84abdbee7765a9
Seung-won Hwang
admin_assigned
2024-06-18T08:38:55.832Z
665ef2f1d2d117e810441ebb
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SeungWon Hwang
user
kevin1113dev
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Dongha Lee
claimed_verified
2024-10-21T12:14:17.465Z
61d4bb8a34cff1c8e64b6a2a
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Dongha Lee
user
donalee
6671145dab84abdbee7765ab
Jinyoung Yeo
admin_assigned
2024-06-18T08:39:28.825Z
6419ad74060a651c415eec62
/avatars/e7b060d0a45e3af1c7439948dbd60e34.svg
Jinyoung Yeom
user
geen02
THEANINE: Revisiting Memory Management in Long-term Conversations with
  Timeline-augmented Response Generation
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/.
Theanine enhances LLM response generation by integrating memory timelines, providing contextual cues for better understanding and recall of past events in dialogues.
memory-augmented response generation large language models LLMs memory timelines contextual cues dialogue systems Theanine TeaFarm counterfactual-driven question-answering TeaBag dataset