推断公司行业部门的可扩展自适应系统：生成式语言模型的 Prompt + 模型调优
A Scalable and Adaptive System to Infer the Industry Sectors of Companies: Prompt + Model Tuning of Generative Language Models
AI总结：
本文提出基于中型生成式语言模型并采用 prompt + 模型调优的公司行业部门推断系统，在主题型 PE 场景中优于常见基线，并具备良好的可扩展性与框架、标注变化适应能力。
摘要：
私募股权（PE）公司通过收购和管理公司来运营投资基金，以在出售时获得高回报。许多 PE 基金是主题型的，这意味着投资专业人员旨在通过覆盖尽可能多的行业部门来识别趋势，并在这些部门中挑选有前景的公司。因此，推断公司所属部门对主题型 PE 基金的成功至关重要。在本研究中，我们对部门框架进行标准化，并讨论典型挑战；随后介绍应对这些挑战的部门推断系统。具体而言，我们的系统构建于一个中型生成式语言模型之上，并通过 prompt + 模型调优流程进行微调。部署的模型表现出优于常见基线的性能。该系统已为众多 PE 专业人员服务一年多，展现出对数据量的强大可扩展性，以及对部门框架和/或标注中任何变化的适应性。
英文摘要：
The Private Equity (PE) firms operate investment funds by acquiring and managing companies to achieve a high return upon selling. Many PE funds are thematic, meaning investment professionals aim to identify trends by covering as many industry sectors as possible, and picking promising companies within these sectors. So, inferring sectors for companies is critical to the success of thematic PE funds. In this work, we standardize the sector framework and discuss the typical challenges; we then introduce our sector inference system addressing these challenges. Specifically, our system is built on a medium-sized generative language model, finetuned with a prompt + model tuning procedure. The deployed model demonstrates a superior performance than the common baselines. The system has been serving many PE professionals for over a year, showing great scalability to data volume and adaptability to any change in sector framework and/or annotation.
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。
