Abstract:
Large Language Models (LLM) have emerged as the most powerful tools to perform
various aspects in daily life. These models are capable of diverse tasks including text
understanding and generating, image generation, language translation and sentiment
analysis. Continual advancements in LLM are expanding the scope of their
capabilities enabling wide range of applications. Although LLMs have made
significant progress, still there are challenges and limitations that needs to be
addressed. As the existing LLM models generally focus on the natural language
processing related tasks, it is crucial to emphasize the training and fine-tuning of
ethical LLMs. When developing and fine-tuning LLMs, issues such as biased
responses and lack of moral consistency can arise. This could lead to significant
ethical challenges, particularly because the data used for training heavily influences
the model’s outputs. Developing a specific ethical LLM by establishing a benchmark
for ethical performance could help overcome this problem. The primary goal of this
research is to implement an ethical inferences language model which can make the
predictions based on the religious data. Religious data is used for the fine-tuning and
Llama-2-7B-chat model is used along with Low Rank Adaptation techniques. The
fine-tuned model was tested by generating the responses to prompts related to ethical
scenarios and the accuracy of the model can be calculated. The model trained with
5000 Bible data. During the training loss decrease gradually by denoting the model
learns well with the data. The fine-tuned model provides reliable performance when
working with ethics-related data. Further the Fine-tuned model demonstrated the
ability to generate text based on ethical prompts, showing a positive trend in the
generated ethical inferences indicating that this model can be developed further by
training with more religious data from Bible, Quran, Hindu scriptures and Tripitaka.
In future the model will be refined further using Supervised Fine Tuning to obtain
more accurate model with enhanced ethical inference capabilities.
