## 5 Discussion  
The evaluation of the six LLMs offers critical insights into selecting the most effective model for the SALLY chatbot. Claude 3.7 Sonnet outperformed its peers across all four criteria, making it the standout candidate. Its exceptional proficiency in Grammar & Syntax is particularly vital for a bilingual chatbot, ensuring clear and credible communication in Greek, a language under-represented in many LLM training datasets. Additionally, its strong Reading Comprehension ensures accurate interpretation of complex official texts, while its adeptness in Sentiment Analysis supports empathetic responses tailored to migrants' emotional needs. Gemini 2.0 Flash also demonstrated notable strengths, particularly in Sentiment Analysis and Grammar & Syntax, positioning it as a viable alternative with well-rounded capabilities suited to diverse tasks. GPT-4o, while robust in most areas, showed a comparative weakness in Sentiment Analysis, though its overall performance remains competitive.  
In contrast, Grok 2, DeepSeek V3, and Llama 3.3 70B exhibited more limited suitability for SALLY requirements. Grok 2 showed promise in Sentiment Analysis but lacked consis  
Figure 2: Comparison of Human and Machine Evaluation across the four criteria.  
tency across other criteria, suggesting it may not reliably meet the chatbot's multifaceted demands. DeepSeek V3 excelled in Summarization yet faltered in Grammar & Syntax and Sentiment Analysis, limiting its versatility. Llama 3.3 70B consistently underperformed, particularly in Grammar & Syntax, with significant variability that undermines its reliability for a migrant support application.  
Based on these findings, Claude 3.7 Sonnet emerges as the leading choice for integration into SALLY, with Gemini 2.0 Flash and GPT-4o as potential alternatives. The final decision will hinge on balancing these performance outcomes with practical considerations such as computational resources, cost, and ease of integration. Further testing, specifically targeting Greek-language proficiency, may refine this selection to ensure optimal performance in SALLY unique context.  
A key observation from this study is the striking alignment between human evaluations and the LLMs' self-assessments. This consistency suggests that machine-powered evaluation could serve as a valuable preliminary tool, particularly for projects like SALLY that operate in low-resource languages and domain-specific settings. Such an approach could streamline initial model screening, reducing time and resource demands while providing a reliable proxy for human judgment. However, its broader applicability requires further exploration across diverse tasks and languages to confirm its robustness.  
Finally, the reliance on evaluations made by experts familiar with asylum processes, residency, and integration challenges, as well as with the linguistic needs and common issues faced by migrants was a deliberate and effective choice for this phase, leveraging their deep familiarity with migrant inquiries and Greek language nuances to assess technical accuracy and practical relevance. This mirrors professional practices in language quality assessment, where native speakers with domain expertise provide authoritative baselines. Yet, this represents only an initial step. Future evaluations plan to involve migrants, the chatbot target users, to gauge usability, effectiveness, and cultural sensitivity from their perspective. This two-phase strategy ensures both linguistic precision and user-centered design, critical for SALLY success.