Author

Baha Rababah

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#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. · arXiv.org · 0 citations

Jul 2026

TopoTuner: Topological Finetuning of Large Language Models

TopoTuner is competitive with full fine-tuning while training only 1-2% of the model parameters, and outperforms LoRA in 7 out of 9 model-dataset settings, which can change up to 39.57% of the projection parameters.

Abdulkadir Erol, Yash Mahajan, Vepaul Hariprashad et al. · arXiv.org · 0 citations