arXiv subjects

Thomas Bayer

Publications and source records attributed to Thomas Bayer.

4 recordsLinked to original sources

arXiv2026

Benchmarking Large Language Models for Safety Data Extraction

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. The evaluation framework assessed accuracy, latency, and cost across more than 50,000 extracted data fields. Results show that text-based extraction consistently outperforms multimodal processing across all metrics. Gemini 1.5 Pro combined with a Chain-of-Thought prompt achieved the highest accuracy (84%), outperforming GPT-4o (81%) and Claude 3.7 Sonnet (79%). However, no model surpassed the 90% accuracy threshold commonly required for reliable real-world deployment. These findings indicate that general-purpose LLMs are not yet robust enough for unsupervised industrial use, though performance suggests strong potential with task-specific fine-tuning. Future research should focus on domain-adapted training, model calibration, and the integration of Human-in-the-Loop verification to ensure safety-critical reliability.

cs.CL↗

arXiv2026

Using Large Language Models and Knowledge Graphs to Improve the Interpretability of Machine Learning Models in Manufacturing

Explaining Machine Learning (ML) results in a transparent and user-friendly manner remains a challenging task of Explainable Artificial Intelligence (XAI). In this paper, we present a method to enhance the interpretability of ML models by using a Knowledge Graph (KG). We store domain-specific data along with ML results and their corresponding explanations, establishing a structured connection between domain knowledge and ML insights. To make these insights accessible to users, we designed a selective retrieval method in which relevant triplets are extracted from the KG and processed by a Large Language Model (LLM) to generate user-friendly explanations of ML results. We evaluated our method in a manufacturing environment using the XAI Question Bank. Beyond standard questions, we introduce more complex, tailored questions that highlight the strengths of our approach. We evaluated 33 questions, analyzing responses using quantitative metrics such as accuracy and consistency, as well as qualitative ones such as clarity and usefulness. Our contribution is both theoretical and practical: from a theoretical perspective, we present a novel approach for effectively enabling LLMs to dynamically access a KG in order to improve the explainability of ML results. From a practical perspective, we provide empirical evidence showing that such explanations can be successfully applied in real-world manufacturing environments, supporting better decision-making in manufacturing processes.

cs.AI↗

arXiv2025

Computations Meet Experiments to Advance the Enzymatic Depolymerization of Plastics One Atom at a Time

Plastics are essential to modern life, yet poor disposal practices contribute to low recycling rates and environmental accumulation-biological degradation and by-product reuse offer a path to mitigate this global threat. This report highlights key insights, future challenges, and research priorities identified during the CECAM workshop "Computations Meet Experiments to Advance the Enzymatic Depolymerization of Plastics One Atom at a Time", held in Trieste from May 6-8, 2025. The workshop brought together an interdisciplinary community of scientists focused on advancing the sustainable use of plastics through enzyme-based degradation. A key point from the discussions is that many bottlenecks in enzymatic recycling arise not only from process engineering challenges, but also from a limited understanding of the underlying molecular mechanisms. We argue that constraints on economic viability and sustainability (e.g., harsh solvents, high temperatures, substrate crystallinity, pretreatments) can-and should-be addressed directly through enzyme design, provided these factors are understood at the molecular level, in synergy with process optimization. For this, it is essential to rely on the integration of experimental and computational approaches to uncover the molecular and mechanistic basis of enzymatic plastic degradation. We highlight how the small-format structure of the workshop, in line with the usual CECAM format, fostered a collaborative, friendly, and relaxed atmosphere. We hope this report encourages future initiatives and the formation of shared consortia to support an open, collaborative, and bio-based plastic recycling community.

q-bio.BM↗

arXiv1996

Domain and Language Independent Feature Extraction for Statistical Text Categorization

A generic system for text categorization is presented which uses a representative text corpus to adapt the processing steps: feature extraction, dimension reduction, and classification. Feature extraction automatically learns features from the corpus by reducing actual word forms using statistical information of the corpus and general linguistic knowledge. The dimension of feature vector is then reduced by linear transformation keeping the essential information. The classification principle is a minimum least square approach based on polynomials. The described system can be readily adapted to new domains or new languages. In application, the system is reliable, fast, and processes completely automatically. It is shown that the text categorizer works successfully both on text generated by document image analysis - DIA and on ground truth data.

cmp-lg↗