## 6 Conclusions  
This paper presents an evaluation of several LLMs to explore their suitability for the SALLY chatbot, an AI agent intended to support migrant integration by providing information in Greek. Greek is considered an under-resourced language in this context, and the application domain is socially sensitive. The analysis, which combined human expert assessments with structured LLM self-evaluations, indicated that Claude 3.7 Sonnet performed strongly, with Gemini 2.0 Flash also showing notable capabilities for addressing the linguistic and informational needs relevant to this application. This research sought to explore several aspects that may contribute to the field. The study attempted a systematic evaluation of LLM performance specifically for the Greek language within the domain of migrant integration support, aiming to address linguistic and contextual challenges faced by migrants. A dual-layered evaluation framework was employed, combining human expert judgment with structured LLM self-evaluation. The application of this hybrid methodology was explored for a low-resource language and a socially impactful field. An aspect examined in this work was an emotion detection dimension, designed with consideration for the psychosocial complexities of migrant communication. This dimension assessed an LLM's capacity to recognize and respond to certain emotional cues, such as anxiety or frustration, which can be an important factor in developing AI for specific user groups. The research also aimed to complement generic benchmarks by grounding the LLM evaluation in domain-relevant tasks derived from official governmental sources, which may enhance the potential applicability of the findings to real-world scenarios. This research offers several findings and methodological considerations. A benchmark of selected LLMs on Greek-language tasks, situated within migrant integration scenarios, is provided, offering insights into their applicability. The development and application of the hybrid evaluation framework are detailed, which integrated human expertise with the analytical capabilities of LLMs, guided by a shared, expert-informed rubric. The study also presents insights into the performance variability of these LLMs across four task types relevant to migrant assistance: reading comprehension of official texts, summarization of information, grammar and syntax correction in Greek, and emotion detection. A notable finding is the indication of LLM potential as preliminary evaluators in specialized contexts. The results showed a strong alignment between LLM self-assessments and human expert judgments when LLMs were provided with detailed, human-designed rubrics, suggesting their utility as a scalable screening tool. Empirical evidence is also presented indicating that carefully designed prompts, based on potential real-world migrant interactions and policy documents, may contribute to improving the faithfulness and contextual appropriateness of LLM-generated outputs in such applications. The deployment of AI in sensitive areas such as migrant support calls for careful attention to ethical considerations, including data privacy, bias mitigation, and transparency concerning AI capabilities and limitations. Therefore, the pre-integration testing conducted in this study is an important step for assessing LLM accuracy and attempting to minimize risks before real-world application. The selected LLM, Claude 3.7 Sonnet, would require further fine-tuning and integration with moderation and fact-checking mechanisms to support the delivery of responses that are accurate, equitable, and trustworthy. While this study focused on the Greek language due to its role in accessing services and integration processes in Greece, and acknowledging the multilingual capabilities of modern LLMs, practical constraints limited the inclusion of a broader range of languages in this phase. Future work could consider the integration of native language support, guided by user feedback and evaluation.