arXiv:2606.11204v1 Announce Type: new Abstract: Accurate extraction of structured information from Safety Data Sheets (SDS) remains challenging in industrial safety due to heterogeneous document formats and the limitations of traditional rule-based methods. This study benchmarks state-of-the-art Large Language Models (LLMs) for automated SDS data extraction, comparing text-based and multimodal processing pipelines. We systematically evaluate four models: Gemini 1.5 Pro, GPT-4o, Claude 3.7 Sonnet, and Llama 3.1-70B, across three prompting strategies: zero-shot, few-shot, and chain-of-thought. T
The rapid advancement of LLMs necessitates robust benchmarking for specific industrial applications like safety data extraction, where accuracy is paramount, thereby pushing research toward practical, high-stakes domain-specific uses.
Accurate, automated safety data extraction mitigates human error and streamlines compliance in hazardous industries, directly impacting operational efficiency and risk management, which are critical for institutional investors and operators.
This research provides a framework for evaluating LLM performance in critical industrial contexts, potentially accelerating the adoption of AI for safety, and setting new benchmarks for 'good enough' performance in regulated fields.
- · AI model developers
- · Chemical industry
- · Industrial safety software providers
- · Regulatory compliance platforms
- · Traditional rule-based data extraction solutions
- · Manual data entry services
- · Human error incidents
Companies will adopt superior LLM-powered safety data extraction tools to enhance operational safety and compliance.
Increased reliance on AI for regulatory reporting could lead to new auditing and validation requirements for AI outputs in industrial safety.
The success in SDS extraction could catalyze broader adoption of multimodal LLMs for complex, document-intensive tasks across other highly regulated industries, transforming information management paradigms.
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Read at arXiv cs.CL