## 3 Results
### Comprehensive Performance Evaluation
#### 3.2.8 Impact of Retrieval-Based Examples on Language Adherence  
In addition to evaluating summarization quality through ROUGE metrics, we measure the Correct Language Rate (CLR), which quantifies the percentage of model outputs correctly written in the intended target language. Table 6 presents a comprehensive comparison of CLR performance between our MuRXLS approach and random sampling baselines across all language pairs and models. Our analysis reveals distinct patterns in language adherence across translation directions:  
* **X\(\rightarrow\)EN Direction Performance:** Both approaches achieve excellent language adherence when generating English summaries, with near-perfect CLR scores across all settings. Zero-shot approaches already achieve 100% CLR for both models in this direction, indicating the inherent strength of LLMs in generating content in English regardless of the input language. MuRXLS provides slight improvements over random sampling in specific language pairs, particularly for Marathi\(\rightarrow\)English and Pashto\(\rightarrow\)English where improvements of +0.87 percentage points are observed. * **EN\(\rightarrow\)X Direction Performance:*
* In this more challenging direction, MuRXLS demonstrates significant improvements over random sampling for most language pairs:
* Mistral-7B shows substantial CLR gains with MuRXLS for Thai (+0.46 to +0.99), Gujarati (+1.16 to +0.96), Marathi (+3.34 to +3.45), Pashto (+4.63 to +1.10), and especially Burmese, where the improvement reaches +7.24 percentage points in the two-shot setting. * GPT-3.5 demonstrates strong improvements with MuRXLS for Gujarati (+2.04 in one-shot, +0.70 in two-shot), Marathi (+0.84 in one-shot, +0.76 in two-shot), and Pashto (+0.68 in one-shot, +1.53 in two-shot). \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Direction** & **Language Pair** & \begin{tabular}{c} **Token Count** \\ **(3-Shot + Query)** \\ \end{tabular} & \begin{tabular}{c} **Token Increase** \\ **vs. 2-Shot** \\ \end{tabular} & \begin{tabular}{c} **Performance (ROUGE-L)** \\ **Two-Shot** \\ \end{tabular} & \
\begin{tabular}{c} **Theree-Shot** \\ \end{tabular} &
\begin{tabular}{c} **Change** \\ **Change** \\ \end{tabular} \\ \hline \multirow{8}{*}{X\(\rightarrow\)EN} & Th-En & 5163.65 & +1726.80 (+50.2\%) & **10.56** & 10.52 & \(-\)0.04 \\  & Gu-En & 4733.15 & +1581.85 (+50.2\%) & **10.54** & 10.47 & \(-\)0.07 \\  & Ma-En & 4883.25 & +1632.10 (+50.2\%) & 10.68 & **10.78** & +0.10 \\  & Pa-En & 3244.90 & +1084.20 (+50.1\%) & 11.03 & **11.32** & +0.29 \\  & Bu-En & 6302.40 & +2106.25 (+50.2\%) & **12.27** & 12.21 & \(-\)0.06 \\  & Si-En & 5579.40 & +1865.45 (+50.2\%) & **12.26** & 12.15 & \(-\)0.11 \\ \cline{2-6}  & En-Th & 3295.60 & +1101.47 (+50.1\%) & **8.85** & 8.44 & \(-\)0.41 \\  & En-Gu & 3289.00 & +1099.33 (+50.1\%) & 0.93 & **1.76** & +0.83 \\  & En-Ma & 3121.05 & +1043.02 (+50.1\%) & **3.28** & 2.89 & \(-\)0.39 \\  & En-Pa & 2647.95 & +885.12 (+50.1\%) & 0.00 & 0.00 & 0.00 \\  & En-Bu & 3769.25 & +12600.05 (+50.1\%) & **5.31** & 5.31 & \(\phantom{-}\)0.00 \\  & En-Si & 2830.45 & +945.85 (+50.1\%) & **5.80** & 5.32 & \(-\)0.48 \\ \hline \multirow{2}{*}{**GPT-4o Average (X\(\rightarrow\)EN)**} & 4984.45 & +1666.11 (+50.2\%) & 11.22 & 11.24 & +0.02 \\  & **GPT-3.5 Average (X\(\rightarrow\)EN)** & 4984.45 & +1666.11 (+50.2\%) & **11.74** & 10.68 & \(-\)1.06 \\  & **GPT-4o Average (EN\(\rightarrow\)X)** & 3158.88 & +1055.81 (+50.1\%) & **4.03** & 3.95 & \(-\)0.08 \\ \cline{1-1}  & **GPT-3.5 Average (EN\(\rightarrow\)X)** & 3158.88 & +1055.81 (+50.1\%) & **1.82** & 1.71 & \(-\)0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Token count analysis and performance comparison (ROUGE-L) for all 12 language pairs. Three-shot settings require approximately 50% more tokens than two-shot settings while rarely improving performance, and often degrading it significantly. **Bold** values indicate the higher performance between two-shot and three-shot settings.