S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking
  in the Era of LLMs
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.
65090bc3f6a2bf0be7b56cc1
Sarkar Snigdha Sarathi Das
65090bc3f6a2bf0be7b56cc2
Chirag Shah
claimed_verified
2024-04-22T06:56:01.336Z
6624485b0e31d65ecce09748
/avatars/b78085aa72e673e570843b85b116f8bc.svg
Chirag Shah
user
chiragshah
65090bc3f6a2bf0be7b56cc3
Mengting Wan
claimed_verified
2024-03-27T08:46:42.978Z
660328d9738bdf26e72dd454
/avatars/7af51be1cc1dfaaa3a78e0b546d97cf8.svg
Mengting Wan
user
mengtingwan
65090bc3f6a2bf0be7b56cc4
Jennifer Neville
65090bc3f6a2bf0be7b56cc5
Longqi Yang
65090bc3f6a2bf0be7b56cc6
Reid Andersen
65090bc3f6a2bf0be7b56cc7
Georg Buscher
65090bc3f6a2bf0be7b56cc8
Tara Safavi
extracted_pending
2023-09-19T02:47:32.305Z
63ffc744cafc9d54985fdf4f
/avatars/0820cad83df012182cdf4094a7dcf744.svg
Tara Safavi
user
tararootcake
S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking
  in the Era of LLMs
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.
S3-DST, a structured prompting technique with Pre-Analytical Recollection, enhances joint dialogue segmentation and state tracking in open-domain dialogs handled by LLM-based systems.
Dialogue State Tracking Large Language Model open-domain dialogues dialogue segmentation contextual interactions contextual shifts structured prompting technique Pre-Analytical Recollection