---
res:
  bibo_abstract:
  - 'Manufacturers operate under dynamic regulatory conditions, leading to frequently
    changing requirements that must be managed with high effort. AI-based requirements
    extraction using Large Language Models (LLMs) has the potential to support engineers
    in this task by analyzing large documents, for example. Existing evaluation approaches
    in AI-supported requirements engineering mainly assess individual requirements
    and their consistency with source documents, while quality criteria of requirements
    sets are largely neglected. This paper presents a five-step approach to evaluate
    the performance of current LLMs in generating requirements sets: a systematic
    literature review to identify quality criteria of requirements sets (1), exemplary
    generation of requirements sets from the Machinery Directive 2006/42/EC using
    LLMs (2), examination (3), evaluation (4) and comparison (5) of the generated
    requirements sets. The results support engineers in selecting LLMs for creating
    requirement sets and in evaluating future LLMs.@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Iris
      foaf_name: Gräßler, Iris
      foaf_surname: Gräßler
      foaf_workInfoHomepage: http://www.librecat.org/personId=47565
    orcid: 0000-0001-5765-971X
  - foaf_Person:
      foaf_givenName: Jan Niklas
      foaf_name: Pfeifer, Jan Niklas
      foaf_surname: Pfeifer
      foaf_workInfoHomepage: http://www.librecat.org/personId=62841
  bibo_doi: 10.17619/UNIPB/1-2646
  dct_date: 2026^xs_gYear
  dct_language: eng
  dct_publisher: Universitätsbibliothek@
  dct_subject:
  - Requirements Engineering
  - Artificial Intelligence
  - Large Language Models
  - Generative AI
  dct_title: Performance evaluation of large language models in extracting requirements
    sets from technical documents@
...