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Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models
Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems.
The Use of Generative Search Engines for Knowledge Work and Complex Tasks
Until recently, search engines were the predominant method for people to access online information.
TnT-LLM: Text Mining at Scale with Large Language Models
Transforming unstructured text into structured and meaningful forms, organized by useful category labels, is a fundamental step in text mining for downstream analysis and application.
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.
Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies
However, using LLMs to generate a user intent taxonomy and apply it for log analysis can be problematic for two main reasons: (1) such a taxonomy is not externally validated; and (2) there may be an undesirable feedback loop.
