On Increasing the Quality of the Climate Observations Question-Answering System’s Output Data

The development of the climate observations question-answer (QA) information system relies on heterogeneous climate data in various formats (text, numerical, graphic, video, audio, geographic and monitoring data). A mandatory element of such a system is a tool that allows processing and analyzing such data.Searching and retrieving data is a central part of the system in question, since the quality of the generated answer heavily depends on it. The exact way the data is retrieved is critical to the output of a QA system as well as to decision-making problems, since there are situations in which the LLM generates a contextually appropriate but factually incorrect answers that do not match the input. Using correct metrics and algorithms for some data types and incorrect ones for others can cause the permissible threshold of irrelevant data to be exceeded, which in turn can cause the quality of the answers to decrease. Retrieval-augmented generation (RAG) systems can also be used to optimize input data for that task.This work discusses various algorithms for data extraction and document ranking, as well as the possibility of using ensembles of LLM agents in development of the QA system that works with climate data.