Summary of A Fused Large Language Model For Predicting Startup Success, by Abdurahman Maarouf et al.
A Fused Large Language Model for Predicting Startup Success
by Abdurahman Maarouf, Stefan Feuerriegel, Nicolas Pröllochs
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed machine learning approach aims to help investors make informed decisions by predicting the probability of success for startups. The model fuses large language processing with other fundamental information about the startup, such as age and business sector. The researchers develop a tailored language model, train it on 20,172 online profiles from Crunchbase, and evaluate its performance in predicting successful startups. The results show that textual self-descriptions of startups are significant predictors of success, making this approach a valuable tool for investors seeking profitable investment opportunities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Investors want to find the best startup investments. To help with this, researchers created a special computer program that uses words and information about a startup to predict if it will be successful. They looked at lots of online profiles from a website called Crunchbase and found that what startups say about themselves is very important in predicting their success. This tool can help investors make smart decisions. |
Keywords
» Artificial intelligence » Language model » Machine learning » Probability
