4 papers
Uncertainty Quantification for LLM Function-Calling
Zihuiwen Ye, Lukas Aichberger, Michael Kirchhof +5
Large Language Models (LLMs) are increasingly deployed to autonomously solve real-world tasks. A key ingredient for this is the LLM Function-Calling paradigm, a widely used approac…
Likelihood Hacking in Probabilistic Program Synthesis
Jacek Karwowski, Younesse Kaddar, Zihuiwen Ye +3
When language models are trained by reinforcement learning (RL) to write probabilistic programs, they can artificially inflate their marginal-likelihood reward by producing program…
Uncertainty-Aware Step-wise Verification with Generative Reward Models
Zihuiwen Ye, Luckeciano Carvalho Melo, Younesse Kaddar +3
Complex multi-step reasoning tasks, such as solving mathematical problems, remain challenging for large language models (LLMs). While outcome supervision is commonly used, process…
Improving Reward Models with Synthetic Critiques
Zihuiwen Ye, Fraser Greenlee-Scott, Max Bartolo +3
Reward models (RMs) play a critical role in aligning language models through the process of reinforcement learning from human feedback. RMs are trained to predict a score reflectin…