title: THEANINE: Revisiting Memory Management in Long-term Conversations with
    Timeline-augmented Response Generation
summary: 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  outdated memories, we argue that such memories can provide contextual cues that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/.
paper_authors._id: 6671145dab84abdbee7765a2
paper_authors.name: Seo Hyun Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-19T07:39:19.479Z
paper_authors._id: 62e167813eb0730f6217c95e
paper_authors.avatarUrl: /avatars/95a18009f725832646643d6169c2bc08.svg
paper_authors.fullname: Seo Hyun Kim
paper_authors.type: user
paper_authors.user: shkim0116
paper_authors._id: 6671145dab84abdbee7765a3
paper_authors.name: Kai Tzu-iunn Ong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-18T08:17:37.450Z
paper_authors._id: 640ec2fd2f9c7b364d182735
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/640ec2fd2f9c7b364d182735/G5WxQXYEOaVL39rAoOI3Y.jpeg
paper_authors.fullname: kai tzu-iunn ong
paper_authors.type: user
paper_authors.user: ktio
paper_authors._id: 6671145dab84abdbee7765a4
paper_authors.name: Taeyoon Kwon
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-18T08:38:18.918Z
paper_authors._id: 636b529ef796304dd67d139c
paper_authors.avatarUrl: /avatars/7a64d5095fcb1da558b52ad48177ad76.svg
paper_authors.fullname: Taeyoon Kwon
paper_authors.type: user
paper_authors.user: Connoriginal
paper_authors._id: 6671145dab84abdbee7765a5
paper_authors.name: Namyoung Kim
paper_authors._id: 6671145dab84abdbee7765a6
paper_authors.name: Keummin Ka
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-18T08:38:39.991Z
paper_authors._id: 65f90edde7bb1e13196f0be7
paper_authors.avatarUrl: /avatars/8585ca41e6720c84997f060796728654.svg
paper_authors.fullname: Keummin Ka
paper_authors.type: user
paper_authors.user: KEUMMin
paper_authors._id: 6671145dab84abdbee7765a7
paper_authors.name: SeongHyeon Bae
paper_authors._id: 6671145dab84abdbee7765a8
paper_authors.name: Yohan Jo
paper_authors._id: 6671145dab84abdbee7765a9
paper_authors.name: Seung-won Hwang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-18T08:38:55.832Z
paper_authors._id: 665ef2f1d2d117e810441ebb
paper_authors.avatarUrl: /avatars/2b12efb9297d9b61fe6dcd1af83e3748.svg
paper_authors.fullname: SeungWon Hwang
paper_authors.type: user
paper_authors.user: kevin1113dev
paper_authors._id: 6671145dab84abdbee7765aa
paper_authors.name: Dongha Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-21T12:14:17.465Z
paper_authors._id: 61d4bb8a34cff1c8e64b6a2a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/61d4bb8a34cff1c8e64b6a2a/eEXKbsXB9VK1sR8QqNPi7.png
paper_authors.fullname: Dongha Lee
paper_authors.type: user
paper_authors.user: donalee
paper_authors._id: 6671145dab84abdbee7765ab
paper_authors.name: Jinyoung Yeo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-18T08:39:28.825Z
paper_authors._id: 6419ad74060a651c415eec62
paper_authors.avatarUrl: /avatars/e7b060d0a45e3af1c7439948dbd60e34.svg
paper_authors.fullname: Jinyoung Yeom
paper_authors.type: user
paper_authors.user: geen02
paper_title: THEANINE: Revisiting Memory Management in Long-term Conversations with
    Timeline-augmented Response Generation
paper_summary: 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/.
paper_ai_keywords: memory-augmented response generation memory timelines counterfactual-driven question-answering TeaFarm G-Eval TeaBag dataset