10 citations · 29 across the 12 of their papers we have counts for
4 papers · 1 filter
Prompt Engineering Large Language Models' Forecasting Capabilities
Philipp Schoenegger, Cameron R. Jones, Philip E. Tetlock +1
Large language model performance can be improved in a large number of ways. Many such techniques, like fine-tuning or advanced tool usage, are time-intensive and expensive. Althoug…
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,…
When Large Language Models are More PersuasiveThan Incentivized Humans, and Why
Jiacheng Liu, Francesco Salvi, Philipp Schoenegger +37
Large Language Models (LLMs) have been shown to be highly persuasive, but when and why they outperform humans is still an open question. We compare the persuasiveness of two LLMs (…
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…