## 4 Results
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline GBERT (Pretrained) & 0.65 & 0.06 & 0.24 & 0.09 \\ GBERT (Fine-tuned) & 0.82 & 0.07 & 0.19 & 0.10 \\ GELECTRA (Pretrained) & 0.43 & 0.06 & 0.48 & 0.11 \\ GELECTRA (Fine-tuned) & 0.64 & 0.19 & 0.38 & 0.26 \\ GPT-4o & 0.97 & 0.88 & 0.83 & 0.86 \\ Llama-3.2-1B & 0.92 & 0.26 & 0.63 & 0.36 \\ Llama-3.2-3B & 0.96 & 0.78 & 0.81 & 0.80 \\ Llama-3.1-8B & 0.97 & 0.84 & 0.85 & 0.85 \\ Llama-3.1-70B & 0.98 & 0.87 & 0.89 & 0.88 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance metrics of different models for error detection. Following the quantitative evaluation, the qualitative analysis further examines the nature of errors detected and corrected by each model. As can be seen in Figure 3, Llama-8B primarily focuses on surface-level corrections, effectively handling typos and morphological adjustments but struggling with deeper grammatical and semantic errors. It correctly detected and fixed \"small\" (\"small\" \(\rightarrow\) \"mal\", meaning \"time\" or \"once\"), demonstrating strong lexical correction abilities, and identified \"Uchtgeistem\" as incorrect but only modified it to \"Uchtgeistem\" instead of the correct \"Lichtgeistem\" (\"Uchtgeistem\" \(\rightarrow\) \"Lichtgeistem\", meaning \"light spirits\"). This suggests that the model relies on high-probability substitutions, favoring common pluralization patterns rather than meaning-based corrections. Its failure to detect grammatical errors, such as the imperative \"Sieh\" (\"look\") instead of \"Sich\" (\"oneself\"), highlights its limitations in syntactic restructuring. Rather than applying linguistic rules, Llama-8B selects corrections based on frequency in its training data, often improving word forms while missing deeper context. Llama-70B (Figure 4) improves upon Llama-8B, particularly in lexical retrieval and probabilistic grammar correction. It successfully corrected \"Luchtnase\" (\"Luchtnase\" \(\rightarrow\) \"Leuchtnase\" meaning \"glowing nose\"), demonstrating strong lexical retrieval likely derived from a large German corpus. However, its correction of \"cs\" to \"sie\" (\"they/her\") instead of \"es\" (\"it\") suggests it prioritizes high-frequency word choices over strict grammatical rules. While it effectively handles spelling errors due to clear one-to-one mappings, its reliance on frequency-based substitutions results in occasional errors in pronoun selection and syntactic agreement. This tendency indicates that Llama-70B has a better grasp of surface-level language patterns but still lacks deep structural understanding. GPT-40 (Figure 5 demonstrates the strongest grammatical and structural correction capabilities among the three models. It correctly fixed \"cs\" to \"es\", showing a possibly strong grasp of German pronoun structures, and accurately inferred \"zu\" (\"Yu\" \(\rightarrow\) \"zu\", meaning \"to\") from a garbled token, indicating robust syntactic processing. However, it incorrectly changed \"Rentieronkel\" to \"Rentieronkel\" (\"reindeer uncle\") instead of \"Rentiere\" (\"reindeer\" in plural), suggesting a bias toward phonetic and morphological similarity over semantic correctness. While GPT-40 significantly outperforms Llama-8B and Llama-70B in error detection and correction, it still occasionally prioritizes frequent patterns over meaning-driven replacements. Overall, GPT-40 is the most reliable for grammatical error \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Llama-70B** & **Llama-8B** & **GPT-40** \\ \hline
1 & 93.75 & 78.79 & 93.94 \\
2 & 50.00 & 100.00 & 50.00 \\
3 & 96.77 & 86.67 & 96.88 \\
4 & 57.14 & 14.29 & 71.43 \\
5 & 89.47 & 69.09 & 87.93 \\
6 & 94.00 & 83.33 & 91.84 \\
7 & 86.30 & 79.45 & 97.26 \\ \hline Average & 89.68 & 76.92 & 92.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Percentage of detected erroneous tokens that were correctly fixed by each model across different documents. Figure 3: Example of Llama-8B’s GEC performance.