  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.
