Taxonomy and Analysis of Sensitive User Queries in Generative AI Search

Although there has been a growing interest among industries to integrate generative LLMs into their services, limited experiences and scarcity of resources acts as a barrier in launching and servicing large-scale LLM-based conversational services. In this paper, we share our experiences in developing and operating generative AI models within a national-scale search engine, with a specific focus on the sensitiveness of user queries. We propose a taxonomy for sensitive search queries, outline our approaches, and present a comprehensive analysis report on sensitive queries from actual users.

Breaking Down Word Semantics from Pre-trained Language Models through Layer-wise Dimension Selection

Contextual word embeddings obtained from pre-trained language model (PLM) have proven effective for various natural language processing tasks at the word level. However, interpreting the hidden aspects within embeddings, such as syntax and semantics, remains challenging. Disentangled representation learning has emerged as a promising approach, which separates specific aspects into distinct embeddings. Furthermore, different linguistic knowledge is believed to be stored in different layers of PLM. This paper aims to disentangle semantic sense from BERT by applying a binary mask to middle outputs across the layers, without updating pre-trained parameters. The disentangled embeddings are evaluated through binary classification to determine if the target word in two different sentences has the same meaning. Experiments with cased BERT$_{\texttt{base}}$ show that leveraging layer-wise information is effective and disentangling semantic sense further improve performance.