% FIrst experiment Results

% Grid Search Results
\begin{table*}[htbp]
\centering
\begin{tabular}{lcccccccc}
\toprule
\textbf{Model} & \textbf{Acc. (\%)} & \textbf{CE} & \textbf{Comb} & \boldmath{$\alpha$} & \textbf{$r$} & \textbf{LR} & \textbf{Epochs} & \textbf{Batch} \\
\midrule
LLama 3.1 8B & \textbf{98.75} & \textbf{1.5873} & \textbf{50.6250} & 32 & 16 & $2.00 \times 10^{-4}$ & 5 & 1 \\
LLama 3.1 8B & 97.92 & 2.3810 & 44.1432 & 32 & 8 & $2.00 \times 10^{-4}$ & 5 & 1 \\
LLama 3.1 8B & 97.08 & 1.6234 & 49.4198 & 16 & 8 & $2.00 \times 10^{-4}$ & 3 & 1 \\
\midrule
Qwen 3 4B    & \textbf{97.92} & 1.7544 & 48.6375 & 32 & 32 & $2.00 \times 10^{-4}$ & 5 & 2 \\
Qwen 3 4B    & 97.53 & 2.9828 & 40.9321 & 32 & 8 & $2.00 \times 10^{-4}$ & 5 & 1 \\
Qwen 3 4B    & 97.53 & \textbf{0.9968} & \textbf{57.6394} & 32 & 16 & $2.00 \times 10^{-4}$ & 5 & 2 \\
\midrule
Gemma 3 4B   & \textbf{97.08} & 3.9683 & 37.2957 & 32 & 8 & $2.00 \times 10^{-4}$ & 3 & 1 \\
Gemma 3 4B   & 96.67 & \textbf{3.1746} & \textbf{39.7966} & 32 & 16 & $2.00 \times 10^{-4}$ & 3 & 1 \\
Gemma 3 4B   & 96.67 & 15.5556 & 33.5612 & 32 & 16 & $2.00 \times 10^{-4}$ & 5 & 1 \\
\bottomrule
\end{tabular}
\caption{Model performance across various hyperparameter configurations our test set.}
\label{tab:sft_grid}
\end{table*}

% Grid Search Param
\begin{table*}[t]
    \centering
    \begin{tabular}{ll}
        \toprule
        \textbf{Parameter} & \textbf{Values} \\
        \midrule
        LoRA Alpha ($\alpha$) & 8, 16, 32 \\
        LoRA Rank ($r$) & 8, 16, 32 \\
        Learning Rate & $1 \times 10^{-4}$, $2 \times 10^{-4}$ \\
        Epochs & 3, 5 \\
        Batch Size & 1, 2 \\
        \bottomrule
    \end{tabular}
    \caption{Grid search hyperparameters used for supervised fine-tuning with LoRA.}
    \label{tab:grid_search_params}
\end{table*}

% SFT param
% Plausible vs Inplausible
\begin{table*}[htbp]
\centering

\begin{tabular}{llccc}
\toprule
\textbf{Model} & \textbf{Plausibility} & \textbf{Acc (\%)} & \textbf{CE} & \textbf{Comb} \\
\midrule
LLama 3.1 8B & Plausible   & 73.33 & 37.09 & 15.81 \\
             & Implausible & 88.33 & 10.16 & 25.88 \\
\midrule
Qwen 3 4B    & Plausible   & 84.58 & 18.36 & 21.34 \\
             & Implausible & 92.08 & 7.97  & 28.83 \\
\midrule
Gemma 3 4B   & Plausible   & 82.08 & 21.70 & 19.91 \\
             & Implausible & 87.50 & 13.49 & 23.82 \\
\bottomrule
\end{tabular}
\caption{Model performance metrics across Plausible and Implausible conditions, including accuracy, content effect, and combined scores.}
\label{tab:results_plausible_inplausible}
\end{table*}


\begin{table}[htbp]
\centering
\begin{tabular}{ll}
\toprule
\textbf{Hyperparameter} & \textbf{Value} \\
\midrule
Max Sequence Length     & 512 \\
LoRA Alpha ($\alpha$)   & 16 \\
LoRA Rank ($r$)         & 16 \\
LoRA Dropout            & 0.05 \\
Learning Rate           & $2.00 \times 10^{-4}$ \\
Training Epochs         & 3 \\
Batch Size              & 2 \\
Temperature             & 0 \\
\bottomrule
\end{tabular}
\caption{LoRA Fine-tuning hyperarameters}
\label{tab:sft_hyperparams}
\end{table}