4 research outputs found
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
BayesBeat: A Bayesian Deep Learning Approach for Atrial Fibrillation Detection from Noisy Photoplethysmography Data
The increasing popularity of smartwatches as affordable and longitudinal
monitoring devices enables us to capture photoplethysmography (PPG) sensor data
for detecting Atrial Fibrillation (AF) in real-time. A significant challenge in
AF detection from PPG signals comes from the inherent noise in the smartwatch
PPG signals. In this paper, we propose a novel deep learning based approach,
BayesBeat that leverages the power of Bayesian deep learning to accurately
infer AF risks from noisy PPG signals, and at the same time provide the
uncertainty estimate of the prediction. Bayesbeat is efficient, robust,
flexible, and highly scalable which makes it particularly suitable for
deployment in commercially available wearable devices. Extensive experiments on
a recently published large dataset reveal that our proposed method BayesBeat
substantially outperforms the existing state-of-the-art methods.Comment: 8 pages, 5 figure
Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies
Log data can reveal valuable information about how users interact with web
search services, what they want, and how satisfied they are. However, analyzing
user intents in log data is not easy, especially for new forms of web search
such as AI-driven chat. To understand user intents from log data, we need a way
to label them with meaningful categories that capture their diversity and
dynamics. Existing methods rely on manual or ML-based labeling, which are
either expensive or inflexible for large and changing datasets. We propose a
novel solution using large language models (LLMs), which can generate rich and
relevant concepts, descriptions, and examples for user intents. However, using
LLMs to generate a user intent taxonomy and apply it to do log analysis can be
problematic for two main reasons: such a taxonomy is not externally validated,
and there may be an undesirable feedback loop. To overcome these issues, we
propose a new methodology with human experts and assessors to verify the
quality of the LLM-generated taxonomy. We also present an end-to-end pipeline
that uses an LLM with human-in-the-loop to produce, refine, and use labels for
user intent analysis in log data. Our method offers a scalable and adaptable
way to analyze user intents in web-scale log data with minimal human effort. We
demonstrate its effectiveness by uncovering new insights into user intents from
search and chat logs from Bing