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.

Shadow-Oriented Tracking Method for Multi-Target Tracking in Video-SAR

This work focuses on multi-target tracking in Video synthetic aperture radar. Specifically, we refer to tracking based on targets' shadows. Current methods have limited accuracy as they fail to consider shadows' characteristics and surroundings fully. Shades are low-scattering and varied, resulting in missed tracking. Surroundings can cause interferences, resulting in false tracking. To solve these, we propose a shadow-oriented multi-target tracking method (SOTrack). To avoid false tracking, a pre-processing module is proposed to enhance shadows from surroundings, thus reducing their interferences. To avoid missed tracking, a detection method based on deep learning is designed to thoroughly learn shadows' features, thus increasing the accurate estimation. And further, a recall module is designed to recall missed shadows. We conduct experiments on measured data. Results demonstrate that, compared with other methods, SOTrack achieves much higher performance in tracking accuracy-18.4%. And ablation study confirms the effectiveness of the proposed modules.