Explains an unsupervised method for tagging search queries using evidence theory and Python, demonstrated with map query examples.
A technical deep dive into how AI rerankers work, explaining their scoring mechanisms, model architectures, and implementation trade-offs.
An analysis of using LLMs like ChatGPT for academic research, highlighting their utility and inherent risks as research tools.
Explores challenges and methods for evaluating question-answering AI systems when processing long documents like technical manuals or novels.
Introduces rerankers, a lightweight Python library providing a unified interface for various document re-ranking models used in information retrieval pipelines.
An analysis of how Google's search engine, by making information retrieval too easy, has eroded online community engagement and human connection.
Explains how to bootstrap training labels for a semantic search system using initial lexical search and user click data instead of costly human annotation.
