2 papers
cs.LG2025
BEACON: Bayesian Optimal Stopping for Efficient LLM Sampling
Guangya Wan, Zixin Stephen Xu, Sasa Zorc +4
Sampling multiple responses is a common way to improve LLM output quality, but it comes at the cost of additional computation. The key challenge is deciding when to stop generating…
cs.CL2024
Derailer-Rerailer: Adaptive Verification for Efficient and Reliable Language Model Reasoning
Guangya Wan, Yuqi Wu, Hao Wang +3
Large Language Models (LLMs) have shown impressive reasoning capabilities, yet existing prompting methods face a critical trade-off: simple approaches often struggle with complex t…