mlsanityData to 7 Oct 2026

A Fused Large Language Model for Predicting Startup Success

arXiv 2409.03668 ↗First posted 5 Sep 2024cs.LG cs.CLcited by 13

Not on the map. Why?

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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 19 of its 89 references that are in the map. First posted on arXiv 5 Sep 2024.

Builds on

19 of 89 references.

References · 19

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  3. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  4. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  5. 2017Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypescited by 251
  6. 2024When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Methodcited by 201
  7. 2024In-Context Learning with Long-Context Models: An In-Depth Explorationcited by 126
  8. 2023Generative AIcited by 119
  9. 2021Fairness in Credit Scoring: Assessment, Implementation and Profit Implicationscited by 65
  10. 2018Deep learning in business analytics and operations research: Models, applications and managerial implicationscited by 24
  11. 2020The value of text for small business default prediction: A deep learning approachcited by 20
  12. 2017Decision support from financial disclosures with deep neural networks and transfer learningcited by 18
7 more
  1. 2022Algorithmic Fairness in Business Analytics: Directions for Research and Practicecited by 18
  2. 2021Finding the unicorn: Predicting early stage startup success through a hybrid intelligence methodcited by 10
  3. 2018Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecastingcited by 7
  4. 2019Public decision support for low population density areas: An imbalance-aware hyper-ensemble for spatio-temporal crime predictioncited by 6
  5. 2018News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictionscited by 4
  6. 2018Robust identification of email tracking: A machine learning approachcited by 1
  7. 2023Addressing distributional shifts in operations management: The case of order fulfillment in customized productioncited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 8; 2026: 5.

13 papers.

Sort:

By area:

  1. 2025Reasoning-Based AI for Startup Evaluation (R.A.I.S.E.): A Memory-Augmented, Multi-Step Decision Frameworkcited by 3
  2. 2025VCBench: Benchmarking LLMs in Venture Capitalcited by 3
  3. 2026DIALECTIC: A Multi-Agent System for Startup Evaluationcited by 2
  4. 2025Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Datacited by 1
  5. 2025CrunchLLM: Multitask LLMs for Structured Business Reasoning and Outcome Predictioncited by 1
  6. 2025Interpretable Machine Learning for Predicting Startup Funding, Patenting, and Exitscited by 1
  7. 2025Beyond Isolated Investor: Predicting Startup Success via Roleplay-Based Collective Agentscited by 1
  8. 2026Predicting Startup Success Using Large Language Models: A Novel In-Context Learning Approachcited by 1
  9. 2025GenAI in Entrepreneurship: a systematic review of generative artificial intelligence in entrepreneurship research: current issues and future directionscited by 0
  10. 2025Can Large Language Models Improve Venture Capital Exit Timing After IPO?cited by 0
  11. 2026Stock Market Price Prediction using Neural Prophet with Deep Neural Networkcited by 0
  12. 2026Predicting Startup Exit from Textual Descriptors - A Computational Linguistics Frameworkcited by 0
1 more
  1. 2026From Ideas to Actions: A Public-Data Decision-Support Toolchain Across the Venture Lifecyclecited by 0