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Author

Lorenz Sparrenberg

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Review Open access Sep 2026

[Requirements for artificial intelligence scribe systems for medical documentation in ophthalmology].

BACKGROUND Clinical documentation accounts for a substantial proportion of ophthalmologists' working time and increasingly consumes scarce resources. Ambient scribe systems, which automatically transcribe clinical consultations and generate structured clinical notes, could reduce the documentation burden. To date, data...

J. Terheyden, Simon Haas, Corinna Schmalohr et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Evaluating and Benchmarking the System One Model Jev

Jev answers MMLU's calculation-heavy questions more accurately than other MMLU questions (94% vs. 91%), whereas both open models, and all three on C-Eval, find them harder.

Tobias Deußer, L. Sparrenberg, R. Sifa · 4 citations
#artificial intelligence Preprint Sep 2026

Automated Regulatory Compliance Question Answering in Financial Services with Domain-Adapted Retrieval-Augmented Generation

This work studies whether a carefully domain-adapted retrieval-augmented generation pipeline closes the gap between compact and compact model quality in financial institutions under dense, frequently amended rulebooks.

Tobias Deußer, Abhishek Pillai, A. Bariviera et al. · 0 citations
#artificial intelligence Preprint Sep 2026

On the Impact of Anonymization on the Performance of Large Language Models

As large language models are increasingly deployed in sensitive domains, anonymizing input data to protect personally identifiable information has become a critical practice. However, the impact of this anonymization on model utility is not well understood. This paper presents a systematic empirical study of the trade-...

Tobias Deußer, Max Hahnbück, L. Sparrenberg et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Accuracy is Not Enough: A Divergence-Based Approach to Evaluate Fidelity Loss in Quantized LLMs

Deployment of Large Language Models (LLMs) on memory-constrained edge devices relies heavily on aggressive post-training quantization. However, evaluating these models is largely based on zero-shot task accuracy, which depends solely on argmax predictions and is insensitive to changes in the underlying predictive distr...

Shahzeb Qamar, L. Sparrenberg, Christian Bauckhage et al. · 1 citation
Jul 2026

The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs

This work proposes Correctness Agreement, a decision-level metric that can measure the intersection of correct predictions between the base model and its quantized variant, and finds that the base and quantized variants usually have a shift in behavior even when accuracy and perplexity are preserved.

Baha Rababah, Shahzeb Qamar, Lorenz Sparrenberg et al. · 0 citations

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