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.

A significantly complete account of the complexity underlying the computation of relevant solution concepts in compact coalitional games is provided. The starting investigation point is the setting of graph games, about which various long-standing open problems were stated in the literature. The paper gives an answer to most of them, and in addition provides new insights on this setting, by stating a number of complexity results about some relevant generalizations and specializations. The presented results also pave the way towards precisely carving the tractability frontier characterizing computation problems on compact coalitional games.