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.

本文提出了一个基于生成式语言模型的细分领域推断系统，在私募股权机构的行业投资中取得了优良的表现。

一种可扩展和自适应的系统，用于推断公司的行业领域：生成语言模型的 Prompt + 模型调整

A Scalable and Adaptive System to Infer the Industry Sectors of  Companies: Prompt + Model Tuning of Generative Language Models

The core activity of a Private Equity (PE) firm is to invest into companies
in order to provide the investors with profit, usually within 4-7 years. To
invest into a company or not is typically done manually by looking at various
performance indicators of the company and then making a decision often based on
instinct. This process is rather unmanageable given the large number of
companies to potentially invest. Moreover, as more data about company
performance indicators becomes available and the number of different indicators
one may want to consider increases, manual crawling and assessment of
investment opportunities becomes inefficient and ultimately impossible. To
address these issues, this paper proposes a framework for automated data-driven
screening of investment opportunities and thus the recommendation of businesses
to invest in. The framework draws on data from several sources to assess the
financial and managerial position of a company, and then uses an explainable
artificial intelligence (XAI) engine to suggest investment recommendations. The
robustness of the model is validated using different AI algorithms, class
imbalance-handling methods, and features extracted from the available data
sources.

本文提出一个基于数据驱动的框架来自动筛选投资机会，并且推荐可以投资的公司。该框架利用多个数据来源来对公司的财务和管理状况进行评估，并使用可解释性人工智能（XAI）引擎提供投资建议。此模型的健壮性通过不同的人工智能算法，类别不平衡处理方法和从可用数据来源提取的特征进行验证。