## 3 Proposed Method
### Adaptable Behavioural Layer
#### 3.3.5 Models Fine-Tuning  
The fine-tuning of LLMs such as Llama-2, Llama-3, GPT-4o Mini, and GPT-3.5 Turbo was performed with the goal of tailoring these models to the special language and cultural peculiarities of customer evaluations in the South African financial industry. This method used domain-specific data, targeted prompts, and sophisticated techniques such as Low-Rank Adaptation (LoRA) and quantisation to improve model performance in sentiment analysis tasks. The Llama-2 and Llama-3 models (e.g., meta-llama/Llama-2-7b-hf and meta-llama/Meta-Llama-3-8B-Instruct) were fine-tuned using a dataset of annotated customer reviews, reflecting sentiment expressions particular to the South African environment. The fine-tuning was led by a cross-entropy loss function that was intended to reduce classification mistakes. The approach entailed modifying pre-trained models by delivering structured input-output pairs while training. Prompts designed for Few-shot Learning, Chain-of-Thought reasoning, and ReAct were used to align model outputs with task objectives, allowing the models to capture nuanced sentiment cues present in financial reviews, including culturally relevant expressions and industry-specific terminology [37].  
To improve computational efficiency, four-bit quantisation was used to reduce resource needs while retaining model performance [116]. The incorporation of LoRA allowed for parameter-efficient fine-tuning by selectively updating just a subset of model weights, making the process more flexible to resource restrictions [117]. The fine-tuning setup used a learning rate of \(1\times 10^{-4}\), a batch size of 4, gradient accumulation steps set to 8, and a cosine learning rate schedule. These settings were chosen to provide high validation data correctness while keeping the model flexible to a wide range of input changes and developing consumer sentiment patterns. Parallel fine-tuning was carried out on OpenAI models (_gpt-4o-mini-2024-07-18_) and (_gpt-3.5-turbo-0125_) using a similar technique, with extra flexibility offered by OpenAI's fine-tuning API. Training datasets were specially selected for South African financial reviews, and prompts were designed to aid the models' interpretation processes. This design enabled the models to efficiently read regional idioms, customer expectations, and sentiment subtleties specific to the local market.  
The fine-tuning method includes the dynamic modification of hyper-parameters, including the number of epochs, the batch size, and the learning rate. These parameters were automatically tuned depending on dataset properties [118]. The model performance was assessed utilising evaluation criteria such as accuracy, precision, recall, and F1-score, with a focus on generalisation across a variety of review contexts. Both the Llama and GPT models showed considerable gains in sentiment classification accuracy after fine-tuning, demonstrating their utility in domain-specific sentiment analysis. The conversational fine-tuning approaches highlight the necessity of tailoring LLMs to specific industrial settings, especially in sectors where regional and cultural characteristics substantially affect attitude expression. This personalised methodology allows the models to better manage the intricacies of financial assessments, increasing their usefulness as instruments for strategic decision-making in consumer sentiment research.