Recent trends in Generative AI have emerged towards fine-tuning foundational large language models (LLMs) to create domain-specific LLMs for automation and chatbot-like applications. Specialized applications for analytics-heavy domains such as Financial report generation require specific writing styles that comprise compound and creative sentences with minimized hallucinations. In this work, we explore the self-corrective auto-regressive qualities of LLMs to learn creativity in writing styles with minimal prompting. We propose a novel two-stage fine-tuning (FT) strategy wherein in the first stage public domain financial reports are used to train for writing styles while allowing the LLM to hallucinate. In the second stage the examples of hallucinations are manually corrected and further used to fine-tune the LLM. The finally trained LLM learns to generate specific financial report sections using minimal instructions and tabular data inputs while ensuring low fine-tuning costs. Our proposed two-stage fine-tuning boosts the accuracy of financial questions answering by two-folds while reducing hallucinations by over 50%. Also, the fine-tuned model has lower perplexity, improved ROUGE, TER and BLEU scores, higher creativity and knowledge density with lower uncertainty and cross entropy than base LLMs. Thus, the proposed framework can be generalized to train creativity in LLMs by first allowing them to hallucinate.

翻译：近年来，生成式人工智能的趋势逐渐转向对基础大型语言模型（LLMs）进行微调，以创建用于自动化和类聊天机器人应用的领域专用LLMs。对于金融报告生成等分析密集型领域的专业应用，需要特定的写作风格，这种风格包含复合且富有创造性的句子，同时需将幻觉降至最低。在本工作中，我们探索了LLMs的自回归自校正特性，以通过最少的提示学习写作风格中的创造性。我们提出了一种新颖的两阶段微调策略：第一阶段使用公开领域的金融报告来训练写作风格，同时允许LLM产生幻觉；第二阶段则对幻觉示例进行人工校正，并进一步用于微调LLM。最终训练完成的LLM能够学习使用最少的指令和表格数据输入来生成特定的金融报告章节，同时确保较低的微调成本。我们提出的两阶段微调将金融问答的准确性提升了两倍，同时将幻觉减少了50%以上。此外，与基础LLM相比，微调后的模型具有更低的困惑度，更高的ROUGE、TER和BLEU分数，更高的创造性和知识密度，以及更低的不确定性和交叉熵。因此，所提出的框架可以通过首先允许模型产生幻觉，进而推广用于训练LLMs的创造性。