## 4 Results  
This section presents the evaluation outcomes for the six LLMs, assessing their suitability in the SALLY chatbot. The evaluation encompasses both human assessments and machine-powered self-evaluations by the LLMs, focusing on the four criteria. In the human evaluation (Table 2), Claude 3.7 Sonnet led across all criteria with consistent, high-quality performance. It achieved the highest score in Grammar & Syntax (**M**ean = 4.78, **S**tandard **D**eviation = 0.28), reflecting its ability to produce grammatically accurate and well-structured Greek responses with minimal variability, a critical asset for a bilingual chatbot serving migrants. Its Reading Comprehension score (M = 4.32, SD = 0.46) further highlights its strength in accurately interpreting complex texts, such as official migration documents. In Sentiment Analysis (M = 4.41, SD = 0.61), it demonstrated a superior capacity to detect and respond to emotional nuances, essential for empathetic interactions, while its Summarization score (M = 4.11, SD = 0.78) indicates effective distillation of information. GPT-4o followed as a strong contender, delivering robust performance across most criteria. It scored well in Grammar & Syntax (M = 4.21, SD = 0.60) and Reading Comprehension (M = 4.05, SD = 0.68), suggesting reliability in generating accurate and comprehensible responses. However, its slightly higher standard deviations compared to Claude 3.7 Sonnet indicate less consistency, and its Sentiment Analysis score (M = 2.94, SD = 0.55) was notably lower, pointing to a limitation in emotional responsiveness. Gemini 2.0 Flash also performed admirably, particularly in Sentiment Analysis (M = 4.23, SD = 0.90), nearly matching Claude 3.7 Sonnet, though its Summarization score (M = 3.58, SD = 0.87) and higher variability suggest it struggles with consistency in condensing complex content. Grok 2 outperformed Llama 3.3 70B in several areas, notably in Sentiment Analysis (M = 3.88, SD = 0.78), where it surpassed GPT-4o, and tied with DeepSeek V3 in Summarization (M = 3.64, SD = 1.05). However, its variability, especially in Summarization, indicates instability that could undermine its reliability for SALLY needs. DeepSeek V3 showed a surprising edge in Summarization (M = 3.70, SD = 0.68), outperforming both Grob 2 and Llama 3.3 70B, but its lower scores in Grammar & Syntax (M = 2.90, SD = 0.83) and Sentiment Analysis (M = 2.76, SD = 0.97), coupled with higher variability (e.g., SD = 0.90 in Reading Comprehension), suggest it is less suited for comprehensive tasks. Llama 3.3 70B consistently underperformed, with the lowest Grammar & Syntax score (M = 1.47, SD = 0.50) and significant variability in Reading Comprehension (M = 2.23, SD = 1.04), highlighting substantial stability issues that render it the weakest candidate. The machine-powered evaluation (Table 3), where each LLM assessed all responses including its own, mirrored the human rankings, reinforcing the reliability of self-evaluation as a preliminary tool. Claude 3.7 Sonnet again led with outstanding scores in Summarization (M = 4.83, SD = 0.40), Sentiment Analysis (M = 4.66, SD = 0.51), Grammar & Syntax (M = 4.60, SD = 0.38), and Reading Comprehension (M = 4.50, SD = 0.31), showcasing its ability to self-assess accurately and consistently. GPT-4o maintained strong performance, particularly in Reading Comprehension (M = 4.41, SD = 0.49) and Summarization (M = 4.00, SD = 0.00), though its Sentiment Analysis score (M = 3.33, SD = 0.81) remained a weak point. Gemini 2.0 Flash excelled in Summarization (M = 4.33, SD = 0.51) and showed solid Sentiment Analysis (M = 4.16, SD = 0.40), aligning closely with its human evaluation results. Grob 2 performed well in Summarization (M = 4.16, SD = 0.75) and Sentiment Analysis (M = 3.83, SD = 0.40), but its Grammar & Syntax score (M = 3.49, SD = 0.34) trailed the top models.