# A Fused Large Language Model for Predicting Startup Success

arXiv: https://arxiv.org/abs/2409.03668 · HTML: https://mlsanity.com/paper/2409.03668/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 89 references that are in the map.
- First posted on arXiv 5 Sep 2024.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method](https://mlsanity.com/paper/2402.17193/) (2024)
- [In-Context Learning with Long-Context Models: An In-Depth Exploration](https://mlsanity.com/paper/2405.00200/) (2024)
- [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023)
- [Fairness in Credit Scoring: Assessment, Implementation and Profit Implications](https://mlsanity.com/paper/2103.01907/) (2021)
- [Deep learning in business analytics and operations research: Models, applications and managerial implications](https://mlsanity.com/paper/1806.10897/) (2018)
- [The value of text for small business default prediction: A deep learning approach](https://mlsanity.com/paper/2003.08964/) (2020)
- [Decision support from financial disclosures with deep neural networks and transfer learning](https://mlsanity.com/paper/1710.03954/) (2017)
- [Algorithmic Fairness in Business Analytics: Directions for Research and Practice](https://mlsanity.com/paper/2207.10991/) (2022)
- [Finding the unicorn: Predicting early stage startup success through a hybrid intelligence method](https://mlsanity.com/paper/2105.03360/) (2021)
- [Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting](https://mlsanity.com/paper/1812.06175/) (2018)
- [Public decision support for low population density areas: An imbalance-aware hyper-ensemble for spatio-temporal crime prediction](https://mlsanity.com/paper/1902.03237/) (2019)
- [News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions](https://mlsanity.com/paper/1801.07047/) (2018)
- [Robust identification of email tracking: A machine learning approach](https://mlsanity.com/paper/1806.04033/) (2018)
- [Addressing distributional shifts in operations management: The case of order fulfillment in customized production](https://mlsanity.com/paper/2304.11910/) (2023)

## Built on by (most cited first, 13 of 13)

- [Reasoning-Based AI for Startup Evaluation (R.A.I.S.E.): A Memory-Augmented, Multi-Step Decision Framework](https://mlsanity.com/paper/2504.12090/) (2025)
- [VCBench: Benchmarking LLMs in Venture Capital](https://mlsanity.com/paper/2509.14448/) (2025)
- [Interpretable Machine Learning for Predicting Startup Funding, Patenting, and Exits](https://mlsanity.com/paper/2510.09465/) (2025)
- [DIALECTIC: A Multi-Agent System for Startup Evaluation](https://mlsanity.com/paper/2603.12274/) (2026)
- [Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data](https://mlsanity.com/paper/2505.24622/) (2025)
- [CrunchLLM: Multitask LLMs for Structured Business Reasoning and Outcome Prediction](https://mlsanity.com/paper/2509.10698/) (2025)
- [Beyond Isolated Investor: Predicting Startup Success via Roleplay-Based Collective Agents](https://mlsanity.com/paper/2512.22608/) (2025)
- [Predicting Startup Success Using Large Language Models: A Novel In-Context Learning Approach](https://mlsanity.com/paper/2601.16568/) (2026)
- [GenAI in Entrepreneurship: a systematic review of generative artificial intelligence in entrepreneurship research: current issues and future directions](https://mlsanity.com/paper/2505.05523/) (2025)
- [Can Large Language Models Improve Venture Capital Exit Timing After IPO?](https://mlsanity.com/paper/2601.00810/) (2025)
- [Stock Market Price Prediction using Neural Prophet with Deep Neural Network](https://mlsanity.com/paper/2601.05202/) (2026)
- [Predicting Startup Exit from Textual Descriptors - A Computational Linguistics Framework](https://mlsanity.com/paper/2608.00045/) (2026)
- [From Ideas to Actions: A Public-Data Decision-Support Toolchain Across the Venture Lifecycle](https://mlsanity.com/paper/2609.15219/) (2026)

## Abstract (arXiv)

Investors are continuously seeking profitable investment opportunities in startups and, hence, for effective decision-making, need to predict a startup's probability of success. Nowadays, investors can use not only various fundamental information about a startup (e.g., the age of the startup, the number of founders, and the business sector) but also textual description of a startup's innovation and business model, which is widely available through online venture capital (VC) platforms such as Crunchbase. To support the decision-making of investors, we develop a machine learning approach with the aim of locating successful startups on VC platforms. Specifically, we develop, train, and evaluate a tailored, fused large language model to predict startup success. Thereby, we assess to what extent self-descriptions on VC platforms are predictive of startup success. Using 20,172 online profiles from Crunchbase, we find that our fused large language model can predict startup success, with textual self-descriptions being responsible for a significant part of the predictive power. Our work provides a decision support tool for investors to find profitable investment opportunities.