arXiv AI

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

arXiv AI

Jul 29

AI-Assisted Knowledge Access for Legacy Enterprise Asset Management in Energy Operations: A Practical Retrieval System

arXiv:2607. 24792v1 Announce Type: cross Abstract: Energy utilities still run engineering work management, engineering procurement, and inventory processes on long-lived enterprise asset management platforms.

By Dave Mercier, Mishca de Costa, Muhammad Anwar, Mark Randall, Issam Hammad

nlp

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

llms fine-tuning benchmarks

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

llms

arXiv AI

Jul 22

M-RAG: Semantic Key-Value Indexing for Retrieval-Augmented Generation

arXiv:2603. 26667v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) turns external documents into evidence for large language models.

By Xu Sun, Tongkai Xu, Baiheng Xie, Li Huang, Qiang Gao, Kunpeng Zhang

llms rag

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

llms rag benchmarks

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

llms rag fine-tuning

arXiv AI

Jul 2

SchemaRAG: Dynamic Large Schema Reduction for LLM-driven Structured Information Extraction

arXiv:2607. 00008v1 Announce Type: cross Abstract: Extracting structured data from unstructured text using large language models (LLMs) becomes challenging when target schemas are large and complex.

By Sin Yu Bonnie Ho, Arlie Coles, Erik Larsson, Eric Marshall, Nathan Bodenstab, Paul Vozila

llms rag

arXiv AI

Jun 26

Patent Representation Learning via Self-supervision

arXiv:2511. 10657v2 Announce Type: replace-cross Abstract: We study self-supervised patent representation learning with contrastive objectives.

By You Zuo (ALMAnaCH), Kim Gerdes (LISN), Eric Villemonte de La Clergerie (ALMAnaCH), Beno\^it Sagot (ALMAnaCH)

rag benchmarks safety

arXiv AI

Jun 17

Brick-DICL: Dynamic In-Context Learning for Automated Brick Schema Classification

arXiv:2606. 17637v1 Announce Type: new Abstract: Building Management Systems (BMS) are essential for optimizing energy efficiency and operational performance in modern buildings.

By Yiyue Qian, Shinan Zhang, Huan Song, Negin Sokhandan, Hannah Marlowe, Diego Socolinsky

llms rag

arXiv AI

Sep 15

TEAR: Table Extraction with Attribute Recommendation from Texts via Large Language Models

arXiv:2609.15205v1 Announce Type: cross Abstract: Table extraction from texts is an important task for information systems, and recent approaches that prompt large language models (LLMs) with instruc...

By Tong Li, Shuye Ding, Jiachuan Wang, Yongqi Zhang, Shuangyin Li, Lei Chen, Bo Li

llms benchmarks

arXiv AI

Jun 24

AI-PAVE-Br: Leveraging Large Language Models for Enhanced Product Attribute Value Extraction through a Golden Set Approach

arXiv:2606. 24655v1 Announce Type: cross Abstract: The explosive growth and complexity of product data within the dynamic Brazilian e-commerce landscape demand robust and specialized methods for structured information extraction.

By Murilo Gazzola, Hugo Gobato Souto, Samuel Silva, J\'ulia Schubert Peixoto, Felipe Siqueira, Andr\'e Luis Pedroso de Morais, Caio Gomes

llms nlp benchmarks

arXiv AI

Jul 17

CatalogAgent: A Supervisor-mediated Self-Learning System Enabling Context Engineering for GenAI Models

arXiv:2607. 14396v1 Announce Type: new Abstract: Product catalogs are the backbone of e-commerce sites, yet a large number of structured attributes (SAs) -- such as material, color, and shape -- often have missing values.

By Zhu Cheng (Xuan), Zhenming Wang (Xuan), Yu (Xuan), Tang, Dan Liu, Bryan Zhang, Athanasios N. Nikolakopoulos, Pranav Souri Itabada, Jing Zhang, Chih-Chi Chou, Peng Gao, Fatemeh Mansoori, Bharat Bojja, Sarath Chander, Sameer Thombare, Umit Batur, Tarik Arici

llms agents

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