Customer-obsessed science

Applications of large-scale knowledge graphs (KG) in the e-commerce platforms can improve shopping experience for their customers. While existing e-commerce KGs integrate a large volume of concepts or product attributes, they fail to discover user intentions, leaving the gap with how people think, behave, and interact with surrounding world. In this work, we present COSMO, a scalable system to mine user-centric

State-of-the-art speech models may exhibit suboptimal performance in specific population subgroups. Detecting these challenging subgroups is crucial to enhance model robustness and fairness. Traditional methods for subgroup identification typically rely on demographic information such as age, gender, and origin. However, collecting such sensitive data at deployment time can be impractical or unfeasible

We investigate the integration of Large Language Models (LLMs) into query encoders to improve dense retrieval without increasing latency and cost, by circumventing the dependency on LLMs at inference time. SoftQE incorporates knowledge from LLMs by mapping embeddings of input queries to those of the LLM-expanded queries. While improvements over various strong baselines on in-domain MS-MARCO metrics are