arXiv AI By Janek Gro{\ss}, Jens Heidrich

AAS-RAIL: Improving Information Extraction for Asset Administration Shells through Retrieval-Augmented In-Context Learning

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arXiv Computation and Language
Aug 25

Industrial-Instruction: An End-to-End Framework for Building Instruction-Tuning and Benchmark Datasets from Industrial Technical Reports

arXiv:2608.22817v1 Announce Type: new Abstract: Industrial technical reports contain high-value knowledge for maintenance, troubleshooting, and product engineering, but their heterogeneous structure...

By Parsa Bakhtiari, Hassan Bashiri, Alireza Khalilipour, Masoud Nasiripour, Moharram Challenger
arXiv AI
Sep 10

Quality Metrics for LLM-Generated Asset Administration Shells: A Perturbation-Based Evaluation Approach

The paper introduces a perturbation-based framework to evaluate quality metrics for large language model (LLM) generated Asset Administration Shells (AAS). By systematically degrading AAS outputs across multiple dimensions, the study identifies that exact property name matching and value-based recall, along with name-based F1 score, best reflect quality changes. Experiments on 6,400 AAS instances from 200 products across GPT‑4o‑mini, Qwen3, and DeepSeek‑R1 reveal how different perturbations affect metrics and highlight variations among model families and product segments.

By Janek Gro{\ss}, Elena Zentgraf, Jens Heidrich
arXiv AI
Aug 5

Evaluating LLM-Based Goal Extraction in Requirements Engineering: Prompting Strategies and Their Limitations

arXiv:2604. 22207v2 Announce Type: replace-cross Abstract: Due to the textual and repetitive nature of many Requirements Engineering (RE) artefacts, Large Language Models (LLMs) have proven useful to automate their generation and processing.

By Anna Arnaudo, Riccardo Coppola, Maurizio Morisio, Flavio Giobergia, Andrea Bioddo, Angelo Bongiorno, Luca Dadone
arXiv AI
Jul 28

TRACE: Business Rule-Grounded Reasoning Curriculum for Knowledge-Preserving Parametric Tool Retrieval in Enterprise LLMs

arXiv:2607. 22639v1 Announce Type: new Abstract: Parametric retrieval enables LLMs to retrieve tools implicitly by assigning each API a unique virtual token and training the model to generate it via constrained beam search.

By Sai Shruthi Sistla, Ashutosh Hathidara, Christopher Toukmaji, Mayank Shrivastava, Karthikeyan Asokkumar