对话式因子信息检索模型（ConFIRM）
Conversational Factor Information Retrieval Model (ConFIRM)
AI总结：
针对专业领域检索数据稀缺问题，提出基于人格五因素模型生成合成数据的ConFIRM方法，金融领域实验显示微调后的Llama-2-7b模型分类准确率达91%，可提升检索系统准确性与个性化程度。
摘要：
本文提出了对话式因子信息检索方法（ConFIRM），这是一种针对领域特定检索任务微调大语言模型（LLM）的新方法。ConFIRM利用人格五因素模型生成能准确反映目标人群特征的合成数据集，解决了专业领域的数据稀缺问题。我们通过金融领域的案例研究验证了ConFIRM的有效性：使用来自PolyU-Asklora金融科技采纳指数的人格对齐数据微调Llama-2-7b模型。所得模型在金融查询分类任务中达到91%的准确率，在NVIDIA A100 GPU上的平均推理时间为0.61秒。ConFIRM有望在各领域打造更准确、更个性化的AI驱动信息检索系统，有望缓解已部署大语言模型的幻觉和信息过时问题。
英文摘要：
This paper introduces the Conversational Factor Information Retrieval Method (ConFIRM), a novel approach to fine-tuning large language models (LLMs) for domain-specific retrieval tasks. ConFIRM leverages the Five-Factor Model of personality to generate synthetic datasets that accurately reflect target population characteristics, addressing data scarcity in specialized domains. We demonstrate ConFIRM's effectiveness through a case study in the finance sector, fine-tuning a Llama-2-7b model using personality-aligned data from the PolyU-Asklora Fintech Adoption Index. The resulting model achieved 91% accuracy in classifying financial queries, with an average inference time of 0.61 seconds on an NVIDIA A100 GPU. ConFIRM shows promise for creating more accurate and personalized AI-driven information retrieval systems across various domains, potentially mitigating issues of hallucinations and outdated information in LLMs deployed
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。
