Predicting Startup Success Using Large Language Models: A Novel In-Context Learning Approach
This provides a decision-making tool for venture capital firms operating in data-scarce environments, though it is incremental as it adapts existing in-context learning methods to a specific domain.
The paper tackles the problem of predicting early-stage startup success with limited data by proposing a k-nearest-neighbor-based in-context learning framework using large language models, achieving higher accuracy than baselines and showing that high balanced accuracy can be achieved with as few as 50 examples.
Venture capital (VC) investments in early-stage startups that end up being successful can yield high returns. However, predicting early-stage startup success remains challenging due to data scarcity (e.g., many VC firms have information about only a few dozen of early-stage startups and whether they were successful). This limits the effectiveness of traditional machine learning methods that rely on large labeled datasets for model training. To address this challenge, we propose an in-context learning framework for startup success prediction using large language models (LLMs) that requires no model training and leverages only a small set of labeled startups as demonstration examples. Specifically, we propose a novel k-nearest-neighbor-based in-context learning framework, called kNN-ICL, which selects the most relevant past startups as examples based on similarity. Using real-world profiles from Crunchbase, we find that the kNN-ICL approach achieves higher prediction accuracy than supervised machine learning baselines and vanilla in-context learning. Further, we study how performance varies with the number of in-context examples and find that a high balanced accuracy can be achieved with as few as 50 examples. Together, we demonstrate that in-context learning can serve as a decision-making tool for VC firms operating in data-scarce environments.