6 papers
Training Large Language Models to Predict Clinical Events
Benjamin Turtel, Paul Wilczewski, Kris Skotheim
Longitudinal clinical notes contain rich evidence of how patients evolve over time, but converting this signal into training supervision for clinical prediction remains challenging…
Forecasting Supply Chain Disruptions with Foresight Learning
Benjamin Turtel, Paul Wilczewski, Kris Skotheim
Anticipating supply chain disruptions before they materialize is a core challenge for firms and policymakers alike. A key difficulty is learning to reason reliably about infrequent…
Foresight Learning for SEC Risk Prediction
Benjamin Turtel, Paul Wilczewski, Danny Franklin +1
Risk disclosures in SEC filings describe potential adverse events but rarely quantify their likelihood, limiting their usefulness for probabilistic analysis. A central obstacle is…
Future-as-Label: Scalable Supervision from Real-World Outcomes
Benjamin Turtel, Paul Wilczewski, Danny Franklin +1
Time creates free supervision: forecasts about real-world events resolve to verifiable outcomes. The passage of time provides labels that require no annotation. To exploit this str…
Outcome-based Reinforcement Learning to Predict the Future
Benjamin Turtel, Danny Franklin, Kris Skotheim +2
Reinforcement Learning with Verifiable Rewards (RLVR) has been an effective approach for improving Large Language Models' reasoning in domains such as coding and mathematics. Here,…
LLMs Can Teach Themselves to Better Predict the Future
Benjamin Turtel, Danny Franklin, Philipp Schoenegger
We present an outcome-driven fine-tuning framework that enhances the forecasting capabilities of large language models (LLMs) without relying on human-curated reasoning samples. Ou…