## 3 Results
### Comprehensive Performance Evaluation
#### 3.2.2 Direction-Specific Performance Patterns  
Our results reveal a notable directional asymmetry in the effectiveness of retrieval-based example selection. For the X\(\rightarrow\)EN direction (summarizing from low-resource languages to English), MuRXLS consistently outperforms random sampling across all models and language pairs. The average ROUGE-L improvements range from +0.26 to +1.53 points, with particularly substantial gains for Burmese\(\rightarrow\)English (+4.20 points with Llama-3-70B in 1-shot setting) and Sinhala\(\rightarrow\)English (+2.34 points with Llama-3-70B in 2-shot setting).  
In contrast, for the EN\(\rightarrow\)X direction (summarizing from English to low-resource languages), the performance improvements are more modest and variable. While MuRXLS still provides an overall positive impact (+0.17 to +0.35 average improvement), the gains are less consistent across language pairs. This asymmetry suggests that retrieval-based example selection is particularly beneficial when the target language is high-resource (English), where models have stronger generation capabilities.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Direction**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Method**} & \multicolumn{8}{c}{**Language Pair**} & \multirow{2}{*}{**Avg.**} \\ \cline{3-3} \cline{5-10}  & & & **Th-En** & & **Gu-En** & & **Mr-En** & & **Pa-En** & & **Su-En** \\ \hline \multirow{10}{*}{Mistral-7B} & mTS-Base & Fine-tuned & **4.40** & **9.37** & **8.94** & **17.55** & **4.72** & **5.13** & **8.35** \\ \cline{2-10}  & & Zero-shot & 5.81 & 0.32 & 0.22 & 0.00 & 2.03 & 0.06 & 1.41 \\ \cline{2-10}  & & Random & 5.26 & 0.65 & 1.52 & 0.00 & 3.38 & 0.88 & 1.95 \\  & & Random & 5.16 & 0.77 & 1.69 & 0.00 & 2.66 & 0.86 & 1.86 \\  & & MuRXLS & (-shot) & & & & & & & \\ \cline{2-10}  & & MuRXLS & **5.87** (+0.61) & **0.72** (+0.07) & \(\begin{array}{c}1.02\\ -0.50\\ -0.06\end{array}\) & 0.00 (+0.00) & **4.78** (+1.40) & **0.89** (+0.01) & **2.21** (+0.26) \\  & & MuRXLS & 0.12 (+0.05) & \(\begin{array}{c}1.63\\ -0.65\end{array}\) & 0.00 (+0.00) & **4.44** (+1.78) & 1.38 (+0.52) & 2.13 (+0.27) \\ \hline \multirow{10}{*}{Llama-3-70B} & Zero-shot & 4.95 & 0.77 & 0.15 & 0.00 & 2.68 & 3.85 & 2.07 \\ \cline{2-10}  & Random & 7.11 & 0.48 & 0.41 & 0.00 & 1.45 & 3.74 & 2.20 \\ \cline{2-10}  & & Random & 7.21 & 1.09 & 0.11 & 0.00 & 0.79 & 1.64 & 1.81 \\ \cline{2-10}  & & MuRXLS & 7.06 & **1.12** (+0.64) & 0.25 & 0.00 (+0.00) & **3.16** (+1.71) & **3.70** (-0.04) & **2.55** (+0.35) \\ \cline{2-10}  & & MuRXLS & (-0.05) & 0.82 & 0.82 & 0.91 (+0.80) & 0.00 (+0.00) & 1.47 (+0.68) & 1.85 (+0.21) & 2.07 (+0.26) \\ \cline{2-10}  & & Zero-shot & 6.43 & 2.10 & 2.29 & 0.00 & 3.83 & 4.25 & 3.15 \\ \cline{2-10}  & & Random & 7.04 & 1.71 & **3.65** & 0.00 & 5.28 & 4.95 & 3.77 \\ \cline{2-10}  & Random & 9.12 & 1.03 & 3.56 & 0.00 & 3.86 & 5.80 & 3.90 \\ \cline{2-10}  & & MuRXLS & 7.91 (+0.87) & **2.55** (+0.84) & 3.62 & & & & & \\ \cline{2-10}  & & MuRXLS & **8.85** & 0.93 & 3.28 & & & & & \\ \cline{2-10}  & & (-0.27) & (-0.10) & (-0.28) & 0.00 (+0.00) & 5.31 (+1.45) & **5.99** (+0.19) & 4.06 (+0.17) \\ \hline \multirow{10}{*}{GPT-3.5} & Zero-shot & 4.53 & 1.40 & **2.05** & 0.00 & 1.70 & 1.22 & 1.82 \\ \cline{2-10}  & Random & 4.45 & **1.60** & 1.02 & 0.00 & 1.55 & 1.72 & 1.72 \\ \cline{1-1}  & Random & 5.20 & 0.55 & 0.88 & 0.00 & 1.55 & 1.63 & 1.63 \\ \cline{1-1}  & & MuRXLS & 4.37 & 1.49 & & & & & \\ \cline{1-1}  & & (-0.08) & (-0.11) & 1.02 (+0.00) & 0.00 (+0.00) & **3.26** (+1.71) & **2.01** (+0.29) & **2.03** (+0.31) \\ \cline{1-1}  & & MuRXLS & **6.96** (+1.76) & 0.63 (+0.08) & \(\begin{array}{c}0.66\\ -0.22\end{array}\) & 0.66 & & & & & & \\ \cline{1-1