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.

翻译：私募股权公司通过收购和管理企业运作投资基金，旨在出售时获得高额回报。许多私募股权基金具有主题投资性质，这意味着投资专业人士力求通过覆盖尽可能多的行业领域来识别趋势，并在这些领域中遴选出有发展前景的企业。因此，准确推断企业所属行业对主题型私募股权基金的成功至关重要。本研究首先标准化行业框架体系，并探讨典型挑战；随后引入解决这些挑战的行业推断系统。具体而言，该系统基于中等规模的生成式语言模型，采用提示+模型微调流程进行训练。部署后的模型性能显著优于常见基线系统。该系统已为众多私募股权专业人士服务逾一年，展现出对数据量的卓越可扩展性，以及对行业框架和/或标注规则变更的高度适应性。