FiSTECH：在大型语言模型中通过金融风格迁移增强创造力且避免幻觉
FiSTECH: Financial Style Transfer to Enhance Creativity without Hallucinations in LLMs
AI总结：
本研究提出FiSTECH两阶段微调策略，通过先允许LLM产生幻觉再手动纠正，在金融报告生成中显著提升写作创造力并降低幻觉，实现两倍问答准确率提升和超50%幻觉减少。
摘要：
生成式AI的最新趋势是微调基础大型语言模型（LLMs），以创建用于自动化和聊天机器人应用的领域特定LLMs。像金融报告生成这样重分析的领域应用，需要包含复合句和创造性句子且最小化幻觉的特定写作风格。在本研究中，我们探索LLMs的自我纠正自回归特性，以在最少提示下学习写作风格中的创造力。我们提出了一种新颖的两阶段微调（FT）策略：第一阶段使用公共领域金融报告训练写作风格，同时允许LLM产生幻觉；第二阶段手动纠正幻觉示例，并进一步用于微调LLM。最终训练的LLM学会使用最少指令和表格数据输入生成特定金融报告部分，同时确保低微调成本。我们提出的两阶段微调将金融问答准确率提高两倍，同时将幻觉减少超过50%。此外，微调模型具有更低困惑度，改进的ROUGE、TER和BLEU分数，更高创造力和知识密度，以及比基础LLMs更低的不确定性和交叉熵。因此，所提框架可推广用于训练LLMs的创造力，方法是首先允许它们产生幻觉。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
