Towards Standards-Compliant Assistive Technology Product Specifications via LLMs
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Abstract (arXiv)
In the rapidly evolving field of assistive technology (AT), ensuring that products meet national and international standards is essential for user safety, efficacy, and accessibility. In this vision paper, we introduce CompliAT, a pioneering framework designed to streamline the compliance process of AT product specifications with these standards through the innovative use of Large Language Models (LLMs). CompliAT addresses three critical tasks: checking terminology consistency, classifying products according to standards, and tracing key product specifications to standard requirements. We tackle the challenge of terminology consistency to ensure that the language used in product specifications aligns with relevant standards, reducing misunderstandings and non-compliance risks. We propose a novel approach for product classification, leveraging a retrieval-augmented generation model to accurately categorize AT products aligning to international standards, despite the sparse availability of training data. Finally, CompliAT implements a traceability and compliance mechanism from key product specifications to standard requirements, ensuring all aspects of an AT product are thoroughly vetted against the corresponding standards. By semi-automating these processes, CompliAT aims to significantly reduce the time and effort required for AT product standards compliance and uphold quality and safety standards. We outline our planned implementation and evaluation plan for CompliAT.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Apr 2024.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 1.
4 papers.
- 2024Leveraging Graph-RAG and Prompt Engineering to Enhance LLM-Based Automated Requirement Traceability and Compliance Checkscited by 6
- 2025Multi-Stage Retrieval for Operational Technology Cybersecurity Compliance Using Large Language Models: A Railway Casestudycited by 1
- 2025A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standardscited by 1
- 2026Think Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLMs: A Case Study of UN Regulation No. 152cited by 0
- 2026Think Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLMs: A Case Study of UN Regulation No. 15217 Aug 2026
- 2025A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards19 May 2025
- 2025Multi-Stage Retrieval for Operational Technology Cybersecurity Compliance Using Large Language Models: A Railway Casestudy18 Apr 2025
- 2024Leveraging Graph-RAG and Prompt Engineering to Enhance LLM-Based Automated Requirement Traceability and Compliance Checks11 Dec 2024
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- 2025Standard Applicability Judgment and Cross-jurisdictional Reasoning: A RAG-based Framework for Medical Device Compliance
- 2026OmniCompliance-100K: A Multi-Domain, Rule-Grounded, Real-World Safety Compliance Dataset
- 2026Closed-Loop Validation-Repair for Healthcare Interoperability: A Multi-Model Study of Schema Compliance in Clinical LLMs
- 2025Bridging the Gap with Retrieval-Augmented Generation: Making Prosthetic Device User Manuals Available in Marginalised Languages
- 2024Engineering Safety Requirements for Autonomous Driving with Large Language Models
- 2026DriveSafe: A Hierarchical Risk Taxonomy for Safety-Critical LLM-Based Driving Assistants
- 2024Standardize: Aligning Language Models with Expert-Defined Standards for Content Generation
- 2025Safety Compliance: Rethinking LLM Safety Reasoning through the Lens of Compliance