Automating Venture Capital: Founder assessment using LLM-powered segmentation, feature engineering and automated labeling techniques

• URL: http://arxiv.org/abs/2407.04885v1
• Date: Fri, 5 Jul 2024 22:54:13 GMT
• Title: Automating Venture Capital: Founder assessment using LLM-powered segmentation, feature engineering and automated labeling techniques
• Authors: Ekin Ozince, Yiğit Ihlamur,
• Abstract summary: This study explores the application of large language models (LLMs) in venture capital (VC) decision-making. We utilize LLM prompting techniques, like chain-of-thought, to generate features from limited data, then extract insights through statistics and machine learning. Our results reveal potential relationships between certain founder characteristics and success, as well as demonstrate the effectiveness of these characteristics in prediction.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This study explores the application of large language models (LLMs) in venture capital (VC) decision-making, focusing on predicting startup success based on founder characteristics. We utilize LLM prompting techniques, like chain-of-thought, to generate features from limited data, then extract insights through statistics and machine learning. Our results reveal potential relationships between certain founder characteristics and success, as well as demonstrate the effectiveness of these characteristics in prediction. This framework for integrating ML techniques and LLMs has vast potential for improving startup success prediction, with important implications for VC firms seeking to optimize their investment strategies.

Related papers

• The Economic Implications of Large Language Model Selection on Earnings and Return on Investment: A Decision Theoretic Model [0.0]
  We use a decision-theoretic approach to compare the financial impact of different language models. The study reveals how the superior accuracy of more expensive models can, under certain conditions, justify a greater investment. This article provides a framework for companies looking to optimize their technology choices.
  arXiv  Detail & Related papers  (2024-05-27T20:08:41Z)
• Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing [56.75702900542643]
  We introduce AlphaLLM for the self-improvements of Large Language Models. It integrates Monte Carlo Tree Search (MCTS) with LLMs to establish a self-improving loop. Our experimental results show that AlphaLLM significantly enhances the performance of LLMs without additional annotations.
  arXiv  Detail & Related papers  (2024-04-18T15:21:34Z)
• Comprehensive Reassessment of Large-Scale Evaluation Outcomes in LLMs: A Multifaceted Statistical Approach [64.42462708687921]
  Evaluations have revealed that factors such as scaling, training types, architectures and other factors profoundly impact the performance of LLMs. Our study embarks on a thorough re-examination of these LLMs, targeting the inadequacies in current evaluation methods. This includes the application of ANOVA, Tukey HSD tests, GAMM, and clustering technique.
  arXiv  Detail & Related papers  (2024-03-22T14:47:35Z)
• Characterization of Large Language Model Development in the Datacenter [55.9909258342639]
  Large Language Models (LLMs) have presented impressive performance across several transformative tasks. However, it is non-trivial to efficiently utilize large-scale cluster resources to develop LLMs. We present an in-depth characterization study of a six-month LLM development workload trace collected from our GPU datacenter Acme.
  arXiv  Detail & Related papers  (2024-03-12T13:31:14Z)
• Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models [52.98743860365194]
  We propose a new fine-tuning method called Self-Play fIne-tuNing (SPIN) At the heart of SPIN lies a self-play mechanism, where the LLM refines its capability by playing against instances of itself. This sheds light on the promise of self-play, enabling the achievement of human-level performance in LLMs without the need for expert opponents.
  arXiv  Detail & Related papers  (2024-01-02T18:53:13Z)
• Knowledge Editing for Large Language Models: A Survey [51.01368551235289]
  One major drawback of large language models (LLMs) is their substantial computational cost for pre-training. Knowledge-based Model Editing (KME) has attracted increasing attention, which aims to precisely modify the LLMs to incorporate specific knowledge.
  arXiv  Detail & Related papers  (2023-10-24T22:18:13Z)
• Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena [25.865825113847404]
  We introduce AucArena, a novel evaluation suite that simulates auctions. We conduct controlled experiments using state-of-the-art Large Language Models (LLMs) to power bidding agents to benchmark their planning and execution skills.
  arXiv  Detail & Related papers  (2023-10-09T14:22:09Z)
• Startup success prediction and VC portfolio simulation using CrunchBase data [1.7897779505837144]
  This paper focuses on startups at their Series B and Series C investment stages, aiming to predict key success milestones. We introduce novel deep learning model for predicting startup success, integrating a variety of factors such as funding metrics, founder features, industry category. Our work demonstrates the considerable promise of deep learning models and alternative unstructured data in predicting startup success.
  arXiv  Detail & Related papers  (2023-09-27T10:22:37Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)
• Embracing advanced AI/ML to help investors achieve success: Vanguard Reinforcement Learning for Financial Goal Planning [0.0]
  Reinforcement learning is a machine learning approach that can be employed with complex data sets. We will explore the use of machine learning for financial forecasting, predicting economic indicators, and creating a savings strategy.
  arXiv  Detail & Related papers  (2021-10-18T18:46:20Z)
• Graph Neural Network Based VC Investment Success Prediction [11.527912247719915]
  We design an incremental representation learning mechanism and a sequential learning model, utilizing the network structure together with the rich attributes of the nodes. Our method achieves the state-of-the-art prediction performance on a comprehensive dataset of global venture capital investments. It excels at predicting the outcomes for start-ups in industries such as healthcare and IT.
  arXiv  Detail & Related papers  (2021-05-25T14:29:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.