
Table~\ref{tab:consolidated_results} presents a comparison of all intervention methods across model families. 

\begin{table}[htbp]
\small
\centering
\begin{tabular}{lcccc}
\toprule
\textbf{Model} & \textbf{Baseline} & \textbf{SFT} & \textbf{AS-1} & \textbf{AS-2} \\
\midrule
Gemma-3 1B   & 57.92 &  \textbf{76.25} & 56.67 & 55.00 \\
Gemma-3 4B   & 75.42 & \textbf{92.08} & 75.83 & 74.58 \\
Gemma-3 27B  & 84.17 & --             & --    & --    \\
\midrule
LLaMA-3.2 1B & 52.50 & \textbf{66.67} & 52.92 & 47.50 \\
LLaMA-3.2 3B & 68.75 & \textbf{86.25} & 63.33 & 61.25 \\
LLaMA-3.1 8B & 65.00 & \textbf{93.75} & 65.00 & 65.17 \\
\midrule
Qwen3-4B     & 66.25 & \textbf{94.58} & 66.25 & 67.92 \\
Qwen3-30B & 71.67 & --            & --    & --    \\
\bottomrule
\end{tabular}
\caption{\small Test accuracy (\%) across all intervention methods. Baseline: zero-shot performance; SFT: supervised fine-tuning; AS-1: activation steering approach 1; AS-2: activation steering approach 2.}
\label{tab:consolidated_results}
\end{table}



\subsection{SFT Result}

\begin{table}[htbp]
\small
\centering

\begin{tabular}{lrrr}
\toprule
\textbf{Model} & \textbf{Acc} $\uparrow$ & \textbf{CE} $\downarrow$ & \textbf{Comb} $\uparrow$\\
\midrule
Gemma-3 4B   & 91.62 & 13.8298 & 24.7855 \\
\midrule
LLaMA-3.1 8B & 93.71 & 6.3608 & 31.279 \\
\midrule
Qwen3-4B     & 95.28 & 7.4468 & 30.4066 \\
\bottomrule
\end{tabular}
\caption{\small Model Evaluation Results on the test data given by the organizers. Acc = accuracy (\%), CE = content effect bias score, Comb = combined metric.}
\label{tab:SFT_experiment1}
\end{table}

Table \ref{tab:SFT_experiment1} presents the evaluation results of the different models on the test set provided by the organizers. Among the models, Qwen3-4B achieved the highest accuracy at 95.28\%, while LLaMA-3.1 8B reached 93.71\% accuracy with the lowest content effect of 6.3608. We uploaded the LLaMA-3.1 8B model to the final CodaBench evaluation, which secured 34th place in Subtask 1.

For the second experiment applied SFT with LoRa to all pretrained models and optimized the hyperparameters through a grid search. This procedure substantially improved performance on the syllogistic reasoning task compared to the baseline models, yielding consistently high accuracy across architectures. The results also reveal that performance is sensitive to the choice of LoRA rank and scaling parameters, confirming that careful tuning is necessary to obtain optimal behavior for reasoning tasks.

In appendix \ref{app:sft} table \ref{tab:sft_grid} shows that the highest accuracy was achieved by a fine-tuned Llama 3.1 8B with 98.75\%, indicating that this model can learn the target reasoning patterns almost perfectly under the right hyperparameter settings. The lowest content effect, was achieved by a fine-tuned Qwen 3 4B with a score of 0.9968. When considering the combined score, which balances correctness with bias reduction, the same fine-tuned Qwen 3 4B achieved the best overall combined score of 57.6394. Together, these findings suggest that SFT can enhance different aspects of model behavior, with Llama 3.1 8B excelling in accuracy and Qwen 3 4B showing the strongest overall balance between performance and bias reduction. 


\paragraph{Determining the importance of training data}

In appendix \ref{app:sft} table \ref{tab:results_plausible_inplausible} shows that the plausibility of the training data strongly influences both model accuracy and content effect. Across all models, training exclusively on implausible syllogisms leads to higher accuracy and smaller content effect than training on plausible ones. Models trained on plausible data show substantially lower accuracy and larger content effects, indicating greater susceptibility to belief bias, whereas training on implausible data reduces this bias. This pattern suggests that plausible examples encourage reliance on real world knowledge, while implausible examples push models to focus more on formal logical validity. Overall, these results indicate that training data plausibility plays a crucial role in shaping reasoning strategies, with implausible only training producing models that reason more accurately and with less interference from prior world knowledge.


\subsection{Activation Steering Results}

\input{sections/as_results}

\subsection{Logit Lens}

\input{sections/ll_results}

