title: S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking
    in the Era of LLMs
summary: The traditional Dialogue State Tracking (DST) problem aims to track user
  preferences and intents in user-agent conversations. While sufficient for
  task-oriented dialogue systems supporting narrow domain applications, the
  advent of Large Language Model (LLM)-based chat systems has introduced many
  real-world intricacies in open-domain dialogues. These intricacies manifest in
  the form of increased complexity in contextual interactions, extended dialogue
  sessions encompassing a diverse array of topics, and more frequent contextual
  shifts. To handle these intricacies arising from evolving LLM-based chat
  systems, we propose joint dialogue segmentation and state tracking per segment
  in open-domain dialogue systems. Assuming a zero-shot setting appropriate to a
  true open-domain dialogue system, we propose S3-DST, a structured prompting
  technique that harnesses Pre-Analytical Recollection, a novel grounding
  mechanism we designed for improving long context tracking. To demonstrate the
  efficacy of our proposed approach in joint segmentation and state tracking, we
  evaluate S3-DST on a proprietary anonymized open-domain dialogue dataset, as
  well as publicly available DST and segmentation datasets. Across all datasets
  and settings, S3-DST consistently outperforms the state-of-the-art,
  demonstrating its potency and robustness the next generation of LLM-based chat
  systems.
paper_authors._id: 65090bc3f6a2bf0be7b56cc1
paper_authors.name: Sarkar Snigdha Sarathi Das
paper_authors._id: 65090bc3f6a2bf0be7b56cc2
paper_authors.name: Chirag Shah
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-22T06:56:01.336Z
paper_authors._id: 6624485b0e31d65ecce09748
paper_authors.avatarUrl: /avatars/b78085aa72e673e570843b85b116f8bc.svg
paper_authors.fullname: Chirag Shah
paper_authors.type: user
paper_authors.user: chiragshah
paper_authors._id: 65090bc3f6a2bf0be7b56cc3
paper_authors.name: Mengting Wan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-27T08:46:42.978Z
paper_authors._id: 660328d9738bdf26e72dd454
paper_authors.avatarUrl: /avatars/7af51be1cc1dfaaa3a78e0b546d97cf8.svg
paper_authors.fullname: Mengting Wan
paper_authors.type: user
paper_authors.user: mengtingwan
paper_authors._id: 65090bc3f6a2bf0be7b56cc4
paper_authors.name: Jennifer Neville
paper_authors._id: 65090bc3f6a2bf0be7b56cc5
paper_authors.name: Longqi Yang
paper_authors._id: 65090bc3f6a2bf0be7b56cc6
paper_authors.name: Reid Andersen
paper_authors._id: 65090bc3f6a2bf0be7b56cc7
paper_authors.name: Georg Buscher
paper_authors._id: 65090bc3f6a2bf0be7b56cc8
paper_authors.name: Tara Safavi
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2023-09-19T02:47:32.305Z
paper_authors._id: 63ffc744cafc9d54985fdf4f
paper_authors.avatarUrl: /avatars/0820cad83df012182cdf4094a7dcf744.svg
paper_authors.fullname: Tara Safavi
paper_authors.type: user
paper_authors.user: tararootcake
paper_title: S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking
    in the Era of LLMs
paper_summary: The traditional Dialogue State Tracking (DST) problem aims to track user
  preferences and intents in user-agent conversations. While sufficient for
  task-oriented dialogue systems supporting narrow domain applications, the
  advent of Large Language Model (LLM)-based chat systems has introduced many
  real-world intricacies in open-domain dialogues. These intricacies manifest in
  the form of increased complexity in contextual interactions, extended dialogue
  sessions encompassing a diverse array of topics, and more frequent contextual
  shifts. To handle these intricacies arising from evolving LLM-based chat
  systems, we propose joint dialogue segmentation and state tracking per segment
  in open-domain dialogue systems. Assuming a zero-shot setting appropriate to a
  true open-domain dialogue system, we propose S3-DST, a structured prompting
  technique that harnesses Pre-Analytical Recollection, a novel grounding
  mechanism we designed for improving long context tracking. To demonstrate the
  efficacy of our proposed approach in joint segmentation and state tracking, we
  evaluate S3-DST on a proprietary anonymized open-domain dialogue dataset, as
  well as publicly available DST and segmentation datasets. Across all datasets
  and settings, S3-DST consistently outperforms the state-of-the-art,
  demonstrating its potency and robustness the next generation of LLM-based chat
  systems.
paper_ai_keywords: Dialogue State Tracking (DST) Large Language Model (LLM) open-domain dialogues contextual interactions dialogue sessions contextual shifts joint dialogue segmentation and state tracking zero-shot setting S3-DST structured prompting technique Pre-Analytical Recollection grounding mechanism long context tracking open-domain dialogue dataset DST and segmentation datasets