Using large language model(s) in generating automated assistant response(s) Topics: AI (Deep Learning), AIOverviews, LLMO, Probably in use, Retrieval Augmented Generation (RAG), SGE
Knowing When to Ask – Bridging Large Language Models and Data Topics: AI (Deep Learning), AIOverviews, LLMO, Retrieval Augmented Generation (RAG), SGE
Using user input to adapt search results provided for presentation to the user Topics: AIOverviews, LLMO, Probably in use, SERP-Features, SGE
Bridging the Gap: Unpacking the Hidden Challenges in Knowledge Distillation for Online Ranking Systems Topics: AI (Deep Learning), AIOverviews, Data Mining, LLMO, SGE
Attribute First, then Generate: Locally-attributable Grounded Text Generation Topics: AI (Deep Learning), AIOverviews, LLMO, Retrieval Augmented Generation (RAG), SGE
Constructing answers to queries through use of a deep model Topics: AI (Deep Learning), LLMO, Microsoft, Probably in use, Ranking, Retrieval Augmented Generation (RAG), Scoring, Semantic Search, SGE
SEMQA: Semi-Extractive Multi-Source Question Answering Topics: AI (Deep Learning), Retrieval Augmented Generation (RAG), SGE
Manipulating Large Language Models to Increase Product Visibility Topics: AI (Deep Learning), LLMO, Retrieval Augmented Generation (RAG), SGE
Large Search Model: Redefining Search Stack in the Era of LLMs Topics: AI (Deep Learning), LLMO, Microsoft, Retrieval Augmented Generation (RAG), SGE
Rethinking Search: Making Domain Experts out of Dilettantes Topics: AI (Deep Learning), LLMO, Marc Najork, SGE
