1,346 research outputs found
Survey on Evaluation Methods for Dialogue Systems
In this paper we survey the methods and concepts developed for the evaluation
of dialogue systems. Evaluation is a crucial part during the development
process. Often, dialogue systems are evaluated by means of human evaluations
and questionnaires. However, this tends to be very cost and time intensive.
Thus, much work has been put into finding methods, which allow to reduce the
involvement of human labour. In this survey, we present the main concepts and
methods. For this, we differentiate between the various classes of dialogue
systems (task-oriented dialogue systems, conversational dialogue systems, and
question-answering dialogue systems). We cover each class by introducing the
main technologies developed for the dialogue systems and then by presenting the
evaluation methods regarding this class
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
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