Tag: fine-tuning

All the articles with the tag "fine-tuning".

• LLMs Can Teach Themselves to Better Predict the Future

  Published:Mar 9, 2025

  This paper introduces a novel framework for improving the forecasting capabilities of Large Language Models (LLMs) through outcome-driven fine-tuning. The method leverages model self-play to generate diverse reasoning trajectories and probabilistic forecasts for future events. These forecasts are then ranked based on their accuracy compared to actual outcomes, and the model is fine-tuned using Direct Preference Optimization (DPO). The results demonstrate significant accuracy improvements (7-10%) on Phi-4 14B and DeepSeek-R1 14B models, bringing their performance on par with much larger models like GPT-4o, without relying on human-curated reasoning samples. This approach has implications for decision-making across various sectors like finance, policy, and law.