Large Language Model-Assisted Course Search: Parsing Structured Parameters from Natural Language Queries
Year: 2025
Type: conference-paper
Abstract: We propose a method to address the challenge of course discovery on search platforms by employing large language models (LLMs) to parse extended search parameters from natural language queries. We developed a set of algorithms that augment a course search platform prototype by integrating an LLM-based assistant to facilitate 55,000 vocational training sessions. The developed method supports natural language queries and parses optional search parameters. For parameter optionality and to evaluate the feasibility of parameter parsing, we introduce a relevance check mechanism based on cosine similarity. The parsing process was conducted by using a guided generation strategy with grammar-based restrictions to limit the generation possibilities. The developed method enhanced the precision and pertinence of course searches.
Institution: Fraunhofer Institute for Open Communication Systems
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4413291828