## 4 Results
### Comparison with GPT-3.5 Using Dataset-Derived Prompts  
To provide a comprehensive benchmark, we compared our fine-tuned English-Twi translation model against OpenAI's GPT-3.5 and GPT-4, employing both zero-shot and few-shot prompting strategies using examples drawn directly from our dataset. In the zero-shot setting, GPT-3.5 was given only the following instruction:  
**\"Translate the following sentence to Twi: <input_sentence>.\"**  
In the few-shot setup, each model was provided with three English-Twi examples from the training dataset, followed by a new English sentence. This method simulates in-context learning, allowing the models to generalize with minimal examples. Table 10 shows a side-by-side evaluation using SpBLEU, ROUGE-L, WER, and a new metric called TFS (Twi Fluency Score), which measures grammatical fluency and syntactic coherence in Twi translations on a scale of 0-100 (manually rated by two native speakers and averaged). Our evaluation of GPT-3.5 and GPT-4 under both zero-shot and few-shot conditions revealed distinct patterns in their ability to handle English-to-Twi translation. In the zero-shot setting, GPT models struggled considerably with Twi's agglutinative morphology. Common morphemes such as \"_mepe_\" (\"I like\") were often mistranslated or broken into semantically incorrect tokens. Furthermore, zero-shot GPT-3.5 frequently produced translations with severe inaccuracies--highlighted by a WER exceeding 100%, suggesting that more edits were required than the length of the reference. This level of error typically \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **SpBLEU** & **ROUGE-L** & **WER (\%)** & **TFS (\%)** \\ \hline Fine-tuned mT5 (ours) & 71.30 & 62.12 & 68.32 & 82 \\ GPT-3.5 (zero-shot) & 38.41 & 45.23 & 121.67 & 54 \\ GPT-3.5 (few-shot) & 52.17 & 58.34 & 89.25 & 63 \\ GPT-4 (zero-shot) & 47.82 & 53.41 & 97.83 & 68 \\ GPT-4 (few-shot) & 59.36 & 61.22 & 75.41 & 74 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison with GPT-3.5 Using Dataset-Derived Prompts. \begin{table}
\begin{tabular}{c l c c c c c} \hline \hline
**Study** & **Methodology/Approach** & **SpBLEU** & **ROUGE-1** & **ROUGE-2** & **ROUGE-L** & **WER** & **Limitations** \\ \hline \multirow{8}{*}{[PERSON]} & The study trains deeper & & & & & & Deeper models are harder to optimize and require careful tuning to avoid performance degradation. \\  & Transformer and & & & & & & \\  & Bi-RNN encoders for & & & & & & \\  & machine translation, & & & & & & \\  & adjusting the attention & & & & & & \\  & mechanism to improve & & & & & & \\  & optimization and & & & & & & degradation. \\  & BLEU scores. & & & & & & \\ \hline \multirow{8}{*}{Our Study} & Leveraging dynamic & & & & & & \\  & dataset aggregation and & & & & & & \\  & cross-lingual learning, & & & & & & \\  & we optimize translation & & & & & & Needs further improvements on rare words and phrases. \\ \cline{1-1}  & for low-resource & & & & & & phrases. \\ \cline{1-1}  & language data and & & & & & & \\ \cline{1-1}  & incorporating & & & & & & \\ \cline{1-1}  & federated-like & & & & & & \\ \cline{1-1}  & training methods. & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cont._occurred in over 30% of the test sentences, largely due to omission, hallucinated content, or structural breakdowns. On the other hand, few-shot prompting significantly improved performance across the board. GPT-4, for instance, gained over 14 BLEU points when exposed to just three English-Twi example pairs before translating a new input. This demonstrates the effectiveness of in-context learning for moderate-length sentences. However, even in the few-shot setup, GPT models were most reliable when translating short, declarative statements (typically under 15 tokens), and their performance declined with longer or interrogative constructions.