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：近期生成式人工智能的趋势正朝着微调基础大语言模型（LLM）以创建用于自动化和类聊天机器人应用的领域专用LLM发展。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，这种风格需包含复合句与创造性句子，同时最大限度减少幻觉。本研究探索了LLM的自回归自校正特性，旨在通过最小化提示学习写作风格的创造性。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财务报告训练写作风格，同时允许LLM产生幻觉；第二阶段对幻觉示例进行人工校正，并进一步用于微调LLM。最终训练的LLM能够仅使用简要指令和表格数据输入生成特定财务报告章节，同时确保较低的微调成本。我们提出的两阶段微调方法将金融问答准确率提升了两倍，同时将幻觉减少超过50%。此外，与基础LLM相比，微调后的模型具有更低的困惑度、改进的ROUGE、TER和BLEU分数，更高的创造性与知识密度，以及更低的不确定性和交叉熵。因此，所提出的框架可通过首先允许模型产生幻觉的方式，推广至训练LLM的创造性能力。